
'''simple docstring'''





import colorsys
import io

import cva
import matplotlib as mpl
import matplotlib.colors as mplc
import matplotlib.figure as mplfigure
import numpy as np
import torch
from matplotlib.backends.backend_agg import FigureCanvasAgg

from utils import img_tensorize


UpperCAmelCase            = 1_000

class     a :




				def __init__(				self						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					int=1.2,     SCREAMING_SNAKE_CASE_						:					Optional[Any]="g",     SCREAMING_SNAKE_CASE_						:					Any=0.5,     SCREAMING_SNAKE_CASE_						:					int="-",     SCREAMING_SNAKE_CASE_						:					Dict="test_out.jpg",     SCREAMING_SNAKE_CASE_						:					Any=True,     SCREAMING_SNAKE_CASE_						:					Tuple=False,     SCREAMING_SNAKE_CASE_						:					Any=None,     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					str=0.7,     ):
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  Dict			=     img.numpy().astype('''np.uint8'''   )
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  List[Any]			=     img_tensorize(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )

										snake_case,       snake_case							:  Optional[Any]			=     img.shape[1], img.shape[0]
										snake_case							:  int			=     mplfigure.Figure(frameon=SCREAMING_SNAKE_CASE_   )
										snake_case							:  int			=     fig.get_dpi()
										snake_case							:  Optional[int]			=     (width * scale + 1e-2) / dpi
										snake_case							:  List[Any]			=     (height * scale + 1e-2) / dpi
										fig.set_size_inches(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										snake_case							:  Any			=     fig.add_axes([0.0, 0.0, 1.0, 1.0]   )
										ax.axis('''off'''   )
										ax.set_xlim(0.0,     SCREAMING_SNAKE_CASE_   )
										ax.set_ylim(SCREAMING_SNAKE_CASE_   )

										snake_case							:  Union[str, Any]			=     saveas
										snake_case							:  str			=     rgb
										snake_case							:  Tuple			=     pynb
										snake_case							:  Optional[int]			=     img
										snake_case							:  List[str]			=     edgecolor
										snake_case							:  str			=     0.5
										snake_case							:  Optional[Any]			=     linestyle
										snake_case							:  Optional[Any]			=     int(np.sqrt(min(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )   ) * scale // 3   )
										snake_case							:  Any			=     width
										snake_case							:  List[str]			=     height
										snake_case							:  Optional[Any]			=     scale
										snake_case							:  int			=     fig
										snake_case							:  Optional[int]			=     ax
										snake_case							:  List[str]			=     pad
										snake_case							:  Tuple			=     idaobj
										snake_case							:  Tuple			=     idaattr
										snake_case							:  Dict			=     FigureCanvasAgg(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=None   ):
										if color is None:
																snake_case							:  int			=     self.edgecolor
										((snake_case),       (snake_case),       (snake_case),       (snake_case))							:  Optional[int]			=     box
										snake_case							:  List[Any]			=     xa - xa
										snake_case							:  Optional[int]			=     ya - ya
										self.ax.add_patch(
										    mpl.patches.Rectangle(
										        (xa, ya),     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     fill=SCREAMING_SNAKE_CASE_,     edgecolor=SCREAMING_SNAKE_CASE_,     linewidth=self.font_size // 3,     alpha=self.alpha,     linestyle=self.linestyle,     )   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					str=None,     SCREAMING_SNAKE_CASE_						:					Union[str, Any]=None,     SCREAMING_SNAKE_CASE_						:					Optional[int]=None,     SCREAMING_SNAKE_CASE_						:					Tuple=None   ):
										if len(boxes.shape   ) > 2:
																snake_case							:  Dict			=     boxes[0]
										if len(obj_ids.shape   ) > 1:
																snake_case							:  Optional[int]			=     obj_ids[0]
										if len(obj_scores.shape   ) > 1:
																snake_case							:  str			=     obj_scores[0]
										if len(attr_ids.shape   ) > 1:
																snake_case							:  int			=     attr_ids[0]
										if len(attr_scores.shape   ) > 1:
																snake_case							:  Union[str, Any]			=     attr_scores[0]
										if isinstance(SCREAMING_SNAKE_CASE_,     torch.Tensor   ):
																snake_case							:  List[Any]			=     boxes.numpy()
										if isinstance(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   ):
																snake_case							:  str			=     np.array(SCREAMING_SNAKE_CASE_   )
										assert isinstance(SCREAMING_SNAKE_CASE_,     np.ndarray   )
										snake_case							:  Optional[int]			=     np.prod(boxes[:, 2:] - boxes[:, :2],     axis=1   )
										snake_case							:  str			=     np.argsort(-areas   ).tolist()
										snake_case							:  Union[str, Any]			=     boxes[sorted_idxs] if boxes is not None else None
										snake_case							:  Dict			=     obj_ids[sorted_idxs] if obj_ids is not None else None
										snake_case							:  Tuple			=     obj_scores[sorted_idxs] if obj_scores is not None else None
										snake_case							:  List[str]			=     attr_ids[sorted_idxs] if attr_ids is not None else None
										snake_case							:  Optional[int]			=     attr_scores[sorted_idxs] if attr_scores is not None else None

										snake_case							:  Union[str, Any]			=     [self._random_color(maximum=1   ) for _ in range(len(SCREAMING_SNAKE_CASE_   )   )]
										snake_case							:  List[Any]			=     [assigned_colors[idx] for idx in sorted_idxs]
										if obj_ids is not None:
																snake_case							:  Dict			=     self._create_text_labels_attr(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
																for i in range(len(SCREAMING_SNAKE_CASE_   )   ):
																						snake_case							:  Any			=     assigned_colors[i]
																						self.add_box(boxes[i],     SCREAMING_SNAKE_CASE_   )
																						self.draw_labels(labels[i],     boxes[i],     SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					int,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Union[str, Any]   ):
										snake_case,       snake_case,       snake_case,       snake_case							:  Dict			=     box
										snake_case							:  Union[str, Any]			=     (xa, ya)
										snake_case							:  Any			=     (ya - ya) * (xa - xa)
										snake_case							:  Any			=     _SMALL_OBJ * self.scale
										if instance_area < small or ya - ya < 40 * self.scale:
																if ya >= self.height - 5:
																						snake_case							:  Union[str, Any]			=     (xa, ya)
																else:
																						snake_case							:  Dict			=     (xa, ya)

										snake_case							:  List[str]			=     (ya - ya) / np.sqrt(self.height * self.width   )
										snake_case							:  Union[str, Any]			=     self._change_color_brightness(SCREAMING_SNAKE_CASE_,     brightness_factor=0.7   )
										snake_case							:  int			=     np.clip((height_ratio - 0.02) / 0.08 + 1,     1.2,     2   )
										font_size *= 0.75 * self.font_size

										self.draw_text(
										    text=SCREAMING_SNAKE_CASE_,     position=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Optional[Any],     SCREAMING_SNAKE_CASE_						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					Tuple,     SCREAMING_SNAKE_CASE_						:					List[str]="g",     SCREAMING_SNAKE_CASE_						:					List[str]="left",     ):
										snake_case							:  Optional[int]			=     0
										snake_case							:  Tuple			=     self.font_size
										snake_case							:  Tuple			=     np.maximum(list(mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   ),     0.2   )
										snake_case							:  Dict			=     max(0.8,     np.max(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  List[Any]			=     {
										    '''facecolor''': '''black''',
										    '''alpha''': self.alpha,
										    '''pad''': self.pad,
										    '''edgecolor''': '''none''',
										}
										snake_case,       snake_case							:  str			=     position
										self.ax.text(
										    SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     size=font_size * self.scale,     family='''sans-serif''',     bbox=SCREAMING_SNAKE_CASE_,     verticalalignment='''top''',     horizontalalignment=SCREAMING_SNAKE_CASE_,     color=SCREAMING_SNAKE_CASE_,     zorder=10,     rotation=SCREAMING_SNAKE_CASE_,     )




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					Any=None   ):
										if saveas is None:
																snake_case							:  List[str]			=     self.saveas
										if saveas.lower().endswith('''.jpg'''   ) or saveas.lower().endswith('''.png'''   ):
																cva.imwrite(
																    SCREAMING_SNAKE_CASE_,     self._get_buffer()[:, :, ::-1],     )
										else:
																self.fig.savefig(SCREAMING_SNAKE_CASE_   )




				def 						__snake_case				(				self						:					Tuple,     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					str,     SCREAMING_SNAKE_CASE_						:					int   ):
										snake_case							:  int			=     [self.idaobj[i] for i in classes]
										snake_case							:  str			=     [self.idaattr[i] for i in attr_classes]
										snake_case							:  Optional[Any]			=     [
										    F"""{label} {score:.2f} {attr} {attr_score:.2f}"""
										    for label, score, attr, attr_score in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )
										]
										return labels




				def 						__snake_case				(				self						:					Any,     SCREAMING_SNAKE_CASE_						:					List[Any],     SCREAMING_SNAKE_CASE_						:					List[str]   ):
										snake_case							:  Union[str, Any]			=     [self.idaobj[i] for i in classes]
										if scores is not None:
																if labels is None:
																						snake_case							:  Optional[Any]			=     ['''{:.0f}%'''.format(s * 1_00   ) for s in scores]
																else:
																						snake_case							:  str			=     ['''{} {:.0f}%'''.format(SCREAMING_SNAKE_CASE_,     s * 1_00   ) for li, s in zip(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_   )]
										return labels




				def 						__snake_case				(				self						:					Union[str, Any],     SCREAMING_SNAKE_CASE_						:					int=2_55   ):
										snake_case							:  Union[str, Any]			=     np.random.randint(0,     len(_COLORS   )   )
										snake_case							:  Optional[int]			=     _COLORS[idx] * maximum
										if not self.rgb:
																snake_case							:  str			=     ret[::-1]
										return ret




				def 						__snake_case				(				self						:					List[Any]   ):
										if not self.pynb:
																snake_case,       snake_case							:  int			=     self.canvas.print_to_buffer()
																if (width, height) != (self.width, self.height):
																						snake_case							:  List[Any]			=     cva.resize(self.img,     (width, height)   )
																else:
																						snake_case							:  Tuple			=     self.img
										else:
																snake_case							:  List[str]			=     io.BytesIO()  # works for cairo backend
																self.canvas.print_rgba(SCREAMING_SNAKE_CASE_   )
																snake_case,       snake_case							:  Union[str, Any]			=     self.width, self.height
																snake_case							:  Optional[Any]			=     buf.getvalue()
																snake_case							:  List[Any]			=     self.img

										snake_case							:  Union[str, Any]			=     np.frombuffer(SCREAMING_SNAKE_CASE_,     dtype='''uint8'''   )
										snake_case							:  Union[str, Any]			=     buffer.reshape(SCREAMING_SNAKE_CASE_,     SCREAMING_SNAKE_CASE_,     4   )
										snake_case,       snake_case							:  Any			=     np.split(SCREAMING_SNAKE_CASE_,     [3],     axis=2   )

										try:
																import numexpr as ne  # fuse them with numexpr

																snake_case							:  str			=     ne.evaluate('''img * (1 - alpha / 255.0) + rgb * (alpha / 255.0)'''   )
										except ImportError:
																snake_case							:  Dict			=     alpha.astype('''float32'''   ) / 255.0
																snake_case							:  Tuple			=     img * (1 - alpha) + rgb * alpha

										return visualized_image.astype('''uint8'''   )




				def 						__snake_case				(				self						:					List[str],     SCREAMING_SNAKE_CASE_						:					Optional[int],     SCREAMING_SNAKE_CASE_						:					Any   ):
										assert brightness_factor >= -1.0 and brightness_factor <= 1.0
										snake_case							:  Any			=     mplc.to_rgb(SCREAMING_SNAKE_CASE_   )
										snake_case							:  List[str]			=     colorsys.rgb_to_hls(*mplc.to_rgb(SCREAMING_SNAKE_CASE_   )   )
										snake_case							:  Union[str, Any]			=     polygon_color[1] + (brightness_factor * polygon_color[1])
										snake_case							:  List[Any]			=     0.0 if modified_lightness < 0.0 else modified_lightness
										snake_case							:  Dict			=     1.0 if modified_lightness > 1.0 else modified_lightness
										snake_case							:  Dict			=     colorsys.hls_to_rgb(polygon_color[0],     SCREAMING_SNAKE_CASE_,     polygon_color[2]   )
										return modified_color




UpperCAmelCase            = (
    np.array(
        [
            0.000,
            0.447,
            0.741,
            0.850,
            0.325,
            0.098,
            0.929,
            0.694,
            0.125,
            0.494,
            0.184,
            0.556,
            0.466,
            0.674,
            0.188,
            0.301,
            0.745,
            0.933,
            0.635,
            0.078,
            0.184,
            0.300,
            0.300,
            0.300,
            0.600,
            0.600,
            0.600,
            1.000,
            0.000,
            0.000,
            1.000,
            0.500,
            0.000,
            0.749,
            0.749,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.333,
            0.333,
            0.000,
            0.333,
            0.667,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            0.333,
            0.000,
            0.667,
            0.667,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.667,
            0.000,
            1.000,
            1.000,
            0.000,
            0.000,
            0.333,
            0.500,
            0.000,
            0.667,
            0.500,
            0.000,
            1.000,
            0.500,
            0.333,
            0.000,
            0.500,
            0.333,
            0.333,
            0.500,
            0.333,
            0.667,
            0.500,
            0.333,
            1.000,
            0.500,
            0.667,
            0.000,
            0.500,
            0.667,
            0.333,
            0.500,
            0.667,
            0.667,
            0.500,
            0.667,
            1.000,
            0.500,
            1.000,
            0.000,
            0.500,
            1.000,
            0.333,
            0.500,
            1.000,
            0.667,
            0.500,
            1.000,
            1.000,
            0.500,
            0.000,
            0.333,
            1.000,
            0.000,
            0.667,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            0.000,
            1.000,
            0.333,
            0.333,
            1.000,
            0.333,
            0.667,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            0.000,
            1.000,
            0.667,
            0.333,
            1.000,
            0.667,
            0.667,
            1.000,
            0.667,
            1.000,
            1.000,
            1.000,
            0.000,
            1.000,
            1.000,
            0.333,
            1.000,
            1.000,
            0.667,
            1.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.167,
            0.000,
            0.000,
            0.333,
            0.000,
            0.000,
            0.500,
            0.000,
            0.000,
            0.667,
            0.000,
            0.000,
            0.833,
            0.000,
            0.000,
            1.000,
            0.000,
            0.000,
            0.000,
            0.143,
            0.143,
            0.143,
            0.857,
            0.857,
            0.857,
            1.000,
            1.000,
            1.000,
        ]
    )
    .astype(np.floataa)
    .reshape(-1, 3)
)





import tempfile
import unittest

import transformers
from transformers import XGLMConfig, XGLMTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, require_sentencepiece, slow

from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
  import jax
  import jax.numpy as jnp
  import numpy as np

  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )
  from transformers.models.xglm.modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel


if is_torch_available():
  import torch

@require_flax
class   lowerCamelCase						:

       def __init__(      self						,    a__						,    a__=14						,    a__=7						,    a__=True						,    a__=True						,    a__=True						,    a__=99						,    a__=32						,    a__=5						,    a__=4						,    a__=37						,    a__="gelu"						,    a__=0.1						,    a__=0.1						,    a__=5_12						,    a__=0.02						,    a__=None						,    ):






        '''simple docstring'''






        snake_case :			Any							=   parent
        snake_case :			str							=   batch_size
        snake_case :			str							=   seq_length
        snake_case :			str							=   is_training
        snake_case :			List[str]							=   use_input_mask
        snake_case :			str							=   use_labels
        snake_case :			Union[str, Any]							=   vocab_size
        snake_case :			Dict							=   d_model
        snake_case :			List[str]							=   num_hidden_layers
        snake_case :			Optional[int]							=   num_attention_heads
        snake_case :			Optional[Any]							=   ffn_dim
        snake_case :			Dict							=   activation_function
        snake_case :			List[str]							=   activation_dropout
        snake_case :			Any							=   attention_dropout
        snake_case :			str							=   max_position_embeddings
        snake_case :			Optional[int]							=   initializer_range
        snake_case :			Union[str, Any]							=   None
        snake_case :			int							=   0
        snake_case :			int							=   2
        snake_case :			Union[str, Any]							=   1

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   np.clip(ids_tensor([self.batch_size, self.seq_length]						,    self.vocab_size       )						,    3						,    self.vocab_size       )

        snake_case :			Union[str, Any]							=   None
        if self.use_input_mask:
         snake_case :			Tuple							=   random_attention_mask([self.batch_size, self.seq_length]       )

        snake_case :			Optional[Any]							=   XGLMConfig(
            vocab_size=self.vocab_size						,    d_model=self.hidden_size						,    num_layers=self.num_hidden_layers						,    attention_heads=self.num_attention_heads						,    ffn_dim=self.ffn_dim						,    activation_function=self.activation_function						,    activation_dropout=self.activation_dropout						,    attention_dropout=self.attention_dropout						,    max_position_embeddings=self.max_position_embeddings						,    initializer_range=self.initializer_range						,    use_cache=a__						,    bos_token_id=self.bos_token_id						,    eos_token_id=self.eos_token_id						,    pad_token_id=self.pad_token_id						,    )

        return (config, input_ids, input_mask)

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   self.prepare_config_and_inputs()
        snake_case	,       snake_case	,       snake_case :			List[str]							=   config_and_inputs
        snake_case :			List[str]							=   {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
        return config, inputs_dict

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case	,       snake_case :			List[str]							=   self.prepare_config_and_inputs()

        snake_case :			Tuple							=   floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
        snake_case :			Union[str, Any]							=   ids_tensor([self.batch_size, self.seq_length]						,    vocab_size=2       )

        return (
            config,
            input_ids,
            attention_mask,
            encoder_hidden_states,
            encoder_attention_mask,
        )

       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[Any]							=   20
        snake_case :			List[Any]							=   model_class_name(a__       )

        snake_case :			List[Any]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.ones((input_ids.shape[0], max_decoder_length)						,    dtype='''i4'''       )

        snake_case :			Optional[Any]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )
        snake_case :			Tuple							=   model(
            input_ids[:, :-1]						,    attention_mask=a__						,    past_key_values=a__						,    position_ids=a__						,    )

        snake_case :			Dict							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,    dtype='''i4'''       )
        snake_case :			str							=   model(
            input_ids[:, -1:]						,    attention_mask=a__						,    past_key_values=outputs_cache.past_key_values						,    position_ids=a__						,    )

        snake_case :			Dict							=   model(a__       )

        snake_case :			Union[str, Any]							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3						,    msg=f'''Max diff is {diff}'''       )



       def 					UpperCAmelCase					(      self						,    a__						,    a__						,    a__						,    a__       ):






        '''simple docstring'''






        snake_case :			Optional[int]							=   20
        snake_case :			Dict							=   model_class_name(a__       )

        snake_case :			Dict							=   jnp.concatenate(
            [attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1])       )]						,    axis=-1						,    )

        snake_case :			List[str]							=   model.init_cache(input_ids.shape[0]						,    a__       )
        snake_case :			Optional[int]							=   jnp.broadcast_to(
            jnp.arange(input_ids.shape[-1] - 1       )[None, :]						,    (input_ids.shape[0], input_ids.shape[-1] - 1)       )

        snake_case :			Optional[int]							=   model(
            input_ids[:, :-1]						,    attention_mask=a__						,    past_key_values=a__						,    position_ids=a__						,    )
        snake_case :			int							=   jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]						,    dtype='''i4'''       )
        snake_case :			Optional[int]							=   model(
            input_ids[:, -1:]						,    past_key_values=outputs_cache.past_key_values						,    attention_mask=a__						,    position_ids=a__						,    )

        snake_case :			Optional[Any]							=   model(a__						,    attention_mask=a__       )
        snake_case :			Optional[int]							=   np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5])       )       )
        self.parent.assertTrue(diff < 1e-3						,    msg=f'''Max diff is {diff}'''       )







@require_sentencepiece
@require_flax
class   lowerCamelCase						(	SCREAMING_SNAKE_CASE,      SCREAMING_SNAKE_CASE,      unittest.TestCase  ):
       _UpperCamelCase     		=   (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
       _UpperCamelCase     		=   (FlaxXGLMForCausalLM,) if is_flax_available() else ()

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   FlaxXGLMModelTester(self       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			str							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward(a__						,    a__						,    a__						,    a__       )

       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case	,       snake_case	,       snake_case :			int							=   self.model_tester.prepare_config_and_inputs()
         self.model_tester.check_use_cache_forward_with_attn_mask(
             a__						,    a__						,    a__						,    a__       )

       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case :			Any							=   XGLMTokenizer.from_pretrained('''XGLM'''						,    padding_side='''left'''       )
        snake_case :			Optional[int]							=   tokenizer(['''Hello this is a long string''', '''Hey''']						,    return_tensors='''np'''						,    padding=a__						,    truncation=a__       )

        snake_case :			List[Any]							=   FlaxXGLMForCausalLM.from_pretrained('''facebook/xglm-564M'''       )
        snake_case :			Optional[int]							=   1
        snake_case :			str							=   False

        snake_case :			str							=   jax.jit(model.generate       )

        snake_case :			Dict							=   jit_generate(inputs['''input_ids''']						,    attention_mask=inputs['''attention_mask''']       ).sequences

        snake_case :			Optional[Any]							=   tokenizer.batch_decode(a__						,    skip_special_tokens=a__       )

        snake_case :			Tuple							=   [
            '''Hello this is a long string of questions, but I\'m not sure if I\'m''',
            '''Hey, I\'m a newbie to the forum and I\'''',
        ]

        self.assertListEqual(a__						,    a__       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			Union[str, Any]							=   self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__       ):
          # prepare inputs
          snake_case :			Union[str, Any]							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			int							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          snake_case :			Any							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
          snake_case :			Optional[Any]							=   getattr(a__						,    a__       )

          snake_case	,       snake_case :			Optional[Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			Tuple							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[int]							=   1
           snake_case :			Optional[Any]							=   0
           snake_case :			Optional[Any]							=   1
          snake_case :			Dict							=   pt_model_class(a__       ).eval()
          # Flax models don't use the `use_cache` option and cache is not returned as a default.
          # So we disable `use_cache` here for PyTorch model.
          snake_case :			Any							=   False
          snake_case :			str							=   model_class(a__						,    dtype=jnp.floataa       )
          snake_case :			Optional[int]							=   convert_pytorch_state_dict_to_flax(pt_model.state_dict()						,    a__       )
          snake_case :			Optional[int]							=   fx_state

          with torch.no_grad():
           snake_case :			int							=   pt_model(**a__       ).to_tuple()

          snake_case :			Optional[int]							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           pt_model.save_pretrained(a__       )
           snake_case :			Union[str, Any]							=   model_class.from_pretrained(a__						,    from_pt=a__       )

          snake_case :			List[Any]							=   fx_model_loaded(**a__       ).to_tuple()
          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output_loaded, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output_loaded[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

       @is_pt_flax_cross_test
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        snake_case	,       snake_case :			List[Any]							=   self.model_tester.prepare_config_and_inputs_for_common()
        for model_class in self.all_model_classes:
         with self.subTest(model_class.__name__       ):
          # prepare inputs
          snake_case :			str							=   self._prepare_for_class(a__						,    a__       )
          snake_case :			List[Any]							=   {k: torch.tensor(v.tolist()       ) for k, v in prepared_inputs_dict.items()}

          # load corresponding PyTorch class
          snake_case :			Any							=   model_class.__name__[4:]  # Skip the "Flax" at the beginning
          snake_case :			Union[str, Any]							=   getattr(a__						,    a__       )

          snake_case :			List[str]							=   pt_model_class(a__       ).eval()
          snake_case :			Tuple							=   False
          snake_case :			Optional[int]							=   model_class(a__						,    dtype=jnp.floataa       )

          snake_case :			Any							=   load_flax_weights_in_pytorch_model(a__						,    fx_model.params       )
          snake_case	,       snake_case :			Union[str, Any]							=   pt_inputs['''input_ids'''].shape
          snake_case :			List[Any]							=   np.random.randint(0						,    seq_length - 1						,    size=(batch_size,)       )
          for batch_idx, start_index in enumerate(a__       ):
           snake_case :			Union[str, Any]							=   0
           snake_case :			Tuple							=   1
           snake_case :			int							=   0
           snake_case :			Optional[int]							=   1

          # make sure weights are tied in PyTorch
          pt_model.tie_weights()

          with torch.no_grad():
           snake_case :			Optional[int]							=   pt_model(**a__       ).to_tuple()

          snake_case :			Any							=   fx_model(**a__       ).to_tuple()
          self.assertEqual(len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )

          with tempfile.TemporaryDirectory() as tmpdirname:
           fx_model.save_pretrained(a__       )
           snake_case :			int							=   pt_model_class.from_pretrained(a__						,    from_flax=a__       )

          with torch.no_grad():
           snake_case :			int							=   pt_model_loaded(**a__       ).to_tuple()

          self.assertEqual(
              len(a__       )						,    len(a__       )						,    '''Output lengths differ between Flax and PyTorch'''       )
          for fx_output, pt_output in zip(a__						,    a__       ):
           self.assert_almost_equals(fx_output[:, -1]						,    pt_output[:, -1].numpy()						,    4e-2       )



       @slow
       def 					UpperCAmelCase					(      self       ):






        '''simple docstring'''






        for model_class_name in self.all_model_classes:
         snake_case :			str							=   model_class_name.from_pretrained('''facebook/xglm-564M'''       )
         snake_case :			List[Any]							=   model(np.ones((1, 1)       )       )
         self.assertIsNotNone(a__       )






