from __future__ import annotations

import random

from matplotlib import animation
from matplotlib import pyplot as plt

# Frame rate of the animation
a     :			Any					    =						2_0

# Time between time steps in seconds
a     :			Optional[Any]					    =						INTERVAL / 1_0_0_0

class 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__ = 1.0					,	lowerCamelCase__ = 1.0					,	lowerCamelCase__ = "blue"					,	):
       lowerCAmelCase_:       List[str]         =  position_x
       lowerCAmelCase_:       Tuple         =  position_y
       lowerCAmelCase_:       List[str]         =  velocity_x
       lowerCAmelCase_:       Tuple         =  velocity_y
       lowerCAmelCase_:       int         =  mass
       lowerCAmelCase_:       Tuple         =  size
       lowerCAmelCase_:       Dict         =  color




    @property
    def     _a						(						self   ):
       return self.position_x, self.position_y




    @property
    def     _a						(						self   ):
       return self.velocity_x, self.velocity_y




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       self.velocity_x += force_x * delta_time
       self.velocity_y += force_y * delta_time




    def     _a						(						self					,	lowerCamelCase__   ):
       self.position_x += self.velocity_x * delta_time
       self.position_y += self.velocity_y * delta_time





class 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__ = 1.0					,	lowerCamelCase__ = 1.0					,	lowerCamelCase__ = 0.0					,	):
       lowerCAmelCase_:       Optional[Any]         =  bodies
       lowerCAmelCase_:       Union[str, Any]         =  gravitation_constant
       lowerCAmelCase_:       Optional[Any]         =  time_factor
       lowerCAmelCase_:       Any         =  softening_factor




    def __len__(						self   ):
       return len(self.bodies   )




    def     _a						(						self					,	lowerCamelCase__   ):
       for bodya in self.bodies:
          lowerCAmelCase_:       List[Any]         =  0.0
          lowerCAmelCase_:       Union[str, Any]         =  0.0
          for bodya in self.bodies:
             if bodya != bodya:
                lowerCAmelCase_:       int         =  bodya.position_x - bodya.position_x
                lowerCAmelCase_:       str         =  bodya.position_y - bodya.position_y

                # Calculation of the distance using Pythagoras's theorem
                # Extra factor due to the softening technique
                lowerCAmelCase_:       Dict         =  (dif_x**2 + dif_y**2 + self.softening_factor) ** (
                    1 / 2
                )

                # Newton's law of universal gravitation.
                force_x += (
                    self.gravitation_constant * bodya.mass * dif_x / distance**3
                )
                force_y += (
                    self.gravitation_constant * bodya.mass * dif_y / distance**3
                )

            # Update the body's velocity once all the force components have been added
          bodya.update_velocity(lowerCamelCase__					,	lowerCamelCase__					,	delta_time * self.time_factor   )

       # Update the positions only after all the velocities have been updated
       for body in self.bodies:
          body.update_position(delta_time * self.time_factor   )





def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase				):

   '''simple docstring'''





   body_system.update_system(lowercase				)

   # Update the positions of the patches
   for patch, body in zip(lowercase       ,							body_system.bodies				):
      lowerCAmelCase_:       Dict         =  (body.position_x, body.position_y)
def 		snake_case__  (		lowercase       ,							lowercase       ,							lowercase = -1       ,							lowercase = 1       ,							lowercase = -1       ,							lowercase = 1       ,							):

   '''simple docstring'''





   lowerCAmelCase_:       List[Any]         =  plt.figure()
   fig.canvas.set_window_title(lowercase				)
   lowerCAmelCase_:       Dict         =  plt.axes(
       xlim=(x_start, x_end)       ,							ylim=(y_start, y_end)				)  # Set section to be plotted
   plt.gca().set_aspect("equal"				)  # Fix aspect ratio

   # Each body is drawn as a patch by the plt-function
   lowerCAmelCase_:       int         =  [
       plt.Circle((body.position_x, body.position_y)       ,							body.size       ,							fc=body.color				)
       for body in body_system.bodies
   ]

   for patch in patches:
      ax.add_patch(lowercase				)

   # Function called at each step of the animation
   def update(lowercase				) -> list[plt.Circle]:
      update_step(lowercase       ,							lowercase       ,							lowercase				)
      return patches

   lowerCAmelCase_:       Union[str, Any]         =  animation.FuncAnimation(  # noqa: F841
       lowercase       ,							lowercase       ,							interval=lowercase       ,							blit=lowercase				)

   plt.show()
def 		snake_case__  (		):

   '''simple docstring'''





   lowerCAmelCase_:       Tuple         =  0.9700436
   lowerCAmelCase_:       List[str]         =  -0.24308753
   lowerCAmelCase_:       List[Any]         =  0.466203685
   lowerCAmelCase_:       Any         =  0.43236573

   lowerCAmelCase_:       Tuple         =  [
       Body(lowercase       ,							lowercase       ,							lowercase       ,							lowercase       ,							size=0.2       ,							color="red"				),
       Body(-position_x       ,							-position_y       ,							lowercase       ,							lowercase       ,							size=0.2       ,							color="green"				),
       Body(0       ,							0       ,							-2 * velocity_x       ,							-2 * velocity_y       ,							size=0.2       ,							color="blue"				),
   ]
   return BodySystem(lowercase       ,							time_factor=3				)
def 		snake_case__  (		):

   '''simple docstring'''





   lowerCAmelCase_:       Tuple         =  7.3476E22
   lowerCAmelCase_:       List[Any]         =  5.972E24
   lowerCAmelCase_:       str         =  1022
   lowerCAmelCase_:       Optional[Any]         =  384399000
   lowerCAmelCase_:       str         =  6.674E-11

   # Calculation of the respective velocities so that total impulse is zero,
   # i.e. the two bodies together don't move
   lowerCAmelCase_:       int         =  earth_mass * velocity_dif / (earth_mass + moon_mass)
   lowerCAmelCase_:       Tuple         =  moon_velocity - velocity_dif

   lowerCAmelCase_:       Tuple         =  Body(-earth_moon_distance       ,							0       ,							0       ,							lowercase       ,							lowercase       ,							10000000       ,							"grey"				)
   lowerCAmelCase_:       str         =  Body(0       ,							0       ,							0       ,							lowercase       ,							lowercase       ,							50000000       ,							"blue"				)
   return BodySystem([earth, moon]       ,							lowercase       ,							time_factor=1000000				)
def 		snake_case__  (		):

   '''simple docstring'''





   lowerCAmelCase_:       Optional[Any]         =  []
   for _ in range(10				):
      lowerCAmelCase_:       Optional[int]         =  random.uniform(-0.5       ,							0.5				)
      lowerCAmelCase_:       int         =  random.uniform(-0.5       ,							0.5				)

      # Bodies are created pairwise with opposite velocities so that the
      # total impulse remains zero
      bodies.append(
          Body(
              random.uniform(-0.5       ,							0.5				)       ,							random.uniform(-0.5       ,							0.5				)       ,							lowercase       ,							lowercase       ,							size=0.05       ,							)				)
      bodies.append(
          Body(
              random.uniform(-0.5       ,							0.5				)       ,							random.uniform(-0.5       ,							0.5				)       ,							-velocity_x       ,							-velocity_y       ,							size=0.05       ,							)				)
   return BodySystem(lowercase       ,							0.01       ,							10       ,							0.1				)


if __name__ == "__main__":
 plot("""Figure-8 solution to the 3-body-problem""", example_a(), -2, 2, -2, 2)
 plot(
     """Moon's orbit around the earth""",
     example_a(),
     -4_3_0_0_0_0_0_0_0,
     4_3_0_0_0_0_0_0_0,
     -4_3_0_0_0_0_0_0_0,
     4_3_0_0_0_0_0_0_0,
 )
 plot("""Random system with many bodies""", example_a(), -1.5, 1.5, -1.5, 1.5)
import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaConfig, GPTaTokenizer, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, 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

 from transformers.modeling_flax_pytorch_utils import (
     convert_pytorch_state_dict_to_flax,
     load_flax_weights_in_pytorch_model,
 )
 from transformers.models.gpta.modeling_flax_gpta import FlaxGPTaLMHeadModel, FlaxGPTaModel

if is_torch_available():
 import torch

class 				_lowercase						:






    '''simple docstring'''




    def __init__(						self					,	lowerCamelCase__					,	lowerCamelCase__=14					,	lowerCamelCase__=7					,	lowerCamelCase__=True					,	lowerCamelCase__=True					,	lowerCamelCase__=False					,	lowerCamelCase__=True					,	lowerCamelCase__=99					,	lowerCamelCase__=32					,	lowerCamelCase__=5					,	lowerCamelCase__=4					,	lowerCamelCase__=37					,	lowerCamelCase__="gelu"					,	lowerCamelCase__=0.1					,	lowerCamelCase__=0.1					,	lowerCamelCase__=512					,	lowerCamelCase__=0.0_2					,	):
       lowerCAmelCase_:       int         =  parent
       lowerCAmelCase_:       Tuple         =  batch_size
       lowerCAmelCase_:       int         =  seq_length
       lowerCAmelCase_:       Any         =  is_training
       lowerCAmelCase_:       Any         =  use_input_mask
       lowerCAmelCase_:       Optional[Any]         =  use_token_type_ids
       lowerCAmelCase_:       str         =  use_labels
       lowerCAmelCase_:       Tuple         =  vocab_size
       lowerCAmelCase_:       List[Any]         =  hidden_size
       lowerCAmelCase_:       Optional[int]         =  num_hidden_layers
       lowerCAmelCase_:       List[str]         =  num_attention_heads
       lowerCAmelCase_:       int         =  intermediate_size
       lowerCAmelCase_:       Tuple         =  hidden_act
       lowerCAmelCase_:       Dict         =  hidden_dropout_prob
       lowerCAmelCase_:       Dict         =  attention_probs_dropout_prob
       lowerCAmelCase_:       Any         =  max_position_embeddings
       lowerCAmelCase_:       Dict         =  initializer_range
       lowerCAmelCase_:       List[Any]         =  None
       lowerCAmelCase_:       List[Any]         =  vocab_size - 1
       lowerCAmelCase_:       Union[str, Any]         =  vocab_size - 1
       lowerCAmelCase_:       int         =  vocab_size - 1




    def     _a						(						self   ):
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	self.vocab_size   )

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

       lowerCAmelCase_:       Union[str, Any]         =  GPTaConfig(
           vocab_size=self.vocab_size					,	n_embd=self.hidden_size					,	n_layer=self.num_hidden_layers					,	n_head=self.num_attention_heads					,	n_positions=self.max_position_embeddings					,	use_cache=lowerCamelCase__					,	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     _a						(						self   ):
       lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  config_and_inputs
       lowerCAmelCase_:       List[str]         =  {"input_ids": input_ids, "attention_mask": attention_mask}
       return config, inputs_dict




    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  self.prepare_config_and_inputs()

       lowerCAmelCase_:       List[str]         =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]   )
       lowerCAmelCase_:       Tuple         =  ids_tensor([self.batch_size, self.seq_length]					,	vocab_size=2   )

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




    def     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Optional[Any]         =  20
       lowerCAmelCase_:       Union[str, Any]         =  model_class_name(lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       Any         =  jnp.ones((input_ids.shape[0], max_decoder_length)					,	dtype="i4"   )

       lowerCAmelCase_:       Union[str, Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )
       lowerCAmelCase_:       Optional[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Any         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Optional[int]         =  model(
           input_ids[:, -1:]					,	attention_mask=lowerCamelCase__					,	past_key_values=outputs_cache.past_key_values					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       List[Any]         =  model(lowerCamelCase__   )

       lowerCAmelCase_:       List[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     _a						(						self					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   ):
       lowerCAmelCase_:       Union[str, Any]         =  20
       lowerCAmelCase_:       List[str]         =  model_class_name(lowerCamelCase__   )

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

       lowerCAmelCase_:       str         =  model.init_cache(input_ids.shape[0]					,	lowerCamelCase__   )
       lowerCAmelCase_:       List[Any]         =  jnp.broadcast_to(
           jnp.arange(input_ids.shape[-1] - 1   )[None, :]					,	(input_ids.shape[0], input_ids.shape[-1] - 1)   )

       lowerCAmelCase_:       List[Any]         =  model(
           input_ids[:, :-1]					,	attention_mask=lowerCamelCase__					,	past_key_values=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)
       lowerCAmelCase_:       Dict         =  jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]]					,	dtype="i4"   )
       lowerCAmelCase_:       Dict         =  model(
           input_ids[:, -1:]					,	past_key_values=outputs_cache.past_key_values					,	attention_mask=lowerCamelCase__					,	position_ids=lowerCamelCase__					,	)

       lowerCAmelCase_:       Union[str, Any]         =  model(lowerCamelCase__					,	attention_mask=lowerCamelCase__   )

       lowerCAmelCase_:       List[str]         =  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_flax
class 				_lowercase						(    UpperCAmelCase__   ,     UpperCAmelCase__   ,     unittest.TestCase ):






    '''simple docstring'''
    SCREAMING_SNAKE_CASE:							Optional[int]        =						(FlaxGPTaModel, FlaxGPTaLMHeadModel) if is_flax_available() else ()
    SCREAMING_SNAKE_CASE:							Union[str, Any]        =						(FlaxGPTaLMHeadModel,) if is_flax_available() else ()




    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  FlaxGPTaModelTester(self   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Dict         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward(lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_     ,  lowerCAmelCase_     ,  lowerCAmelCase_:       Optional[Any]         =  self.model_tester.prepare_config_and_inputs()
          self.model_tester.check_use_cache_forward_with_attn_mask(
              lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__					,	lowerCamelCase__   )




    @slow
    def     _a						(						self   ):
       lowerCAmelCase_:       Any         =  GPTaTokenizer.from_pretrained("gpt2"					,	pad_token="</s>"					,	padding_side="left"   )
       lowerCAmelCase_:       Optional[int]         =  tokenizer(["Hello this is a long string", "Hey"]					,	return_tensors="np"					,	padding=lowerCamelCase__					,	truncation=lowerCamelCase__   )

       lowerCAmelCase_:       List[Any]         =  FlaxGPTaLMHeadModel.from_pretrained("gpt2"   )
       lowerCAmelCase_:       List[str]         =  False
       lowerCAmelCase_:       int         =  model.config.eos_token_id

       lowerCAmelCase_:       Optional[int]         =  jax.jit(model.generate   )

       lowerCAmelCase_:       Any         =  jit_generate(inputs["input_ids"]					,	attention_mask=inputs["attention_mask"]   ).sequences

       lowerCAmelCase_:       Any         =  tokenizer.batch_decode(lowerCamelCase__					,	skip_special_tokens=lowerCamelCase__   )

       lowerCAmelCase_:       Dict         =  [
           "Hello this is a long string of words. I'm going to start with the first one.\n",
           "Hey, I'm not sure if I'm going to be able to do",
       ]

       self.assertListEqual(lowerCamelCase__					,	lowerCamelCase__   )




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       str         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

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

             lowerCAmelCase_     ,  lowerCAmelCase_:       str         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       str         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       List[str]         =  0
                lowerCAmelCase_:       int         =  1
                lowerCAmelCase_:       List[Any]         =  0
                lowerCAmelCase_:       List[Any]         =  1
             lowerCAmelCase_:       Tuple         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Tuple         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       int         =  convert_pytorch_state_dict_to_flax(pt_model.state_dict()					,	lowerCamelCase__   )
             lowerCAmelCase_:       Optional[int]         =  fx_state

             with torch.no_grad():
                lowerCAmelCase_:       Union[str, Any]         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       List[str]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       Dict         =  model_class.from_pretrained(lowerCamelCase__					,	from_pt=lowerCamelCase__   )

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




    @is_pt_flax_cross_test
    def     _a						(						self   ):
       lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  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
             lowerCAmelCase_:       Any         =  self._prepare_for_class(lowerCamelCase__					,	lowerCamelCase__   )
             lowerCAmelCase_:       int         =  {k: torch.tensor(v.tolist()   ) for k, v in prepared_inputs_dict.items()}

             # load corresponding PyTorch class
             lowerCAmelCase_:       List[str]         =  model_class.__name__[4:]  # Skip the "Flax" at the beginning
             lowerCAmelCase_:       Dict         =  getattr(lowerCamelCase__					,	lowerCamelCase__   )

             lowerCAmelCase_:       Dict         =  pt_model_class(lowerCamelCase__   ).eval()
             lowerCAmelCase_:       Optional[Any]         =  model_class(lowerCamelCase__					,	dtype=jnp.floataa   )

             lowerCAmelCase_:       Optional[Any]         =  load_flax_weights_in_pytorch_model(lowerCamelCase__					,	fx_model.params   )
             lowerCAmelCase_     ,  lowerCAmelCase_:       List[str]         =  pt_inputs["input_ids"].shape
             lowerCAmelCase_:       Optional[Any]         =  np.random.randint(0					,	seq_length - 1					,	size=(batch_size,)   )
             for batch_idx, start_index in enumerate(lowerCamelCase__   ):
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       List[Any]         =  1
                lowerCAmelCase_:       Dict         =  0
                lowerCAmelCase_:       Optional[int]         =  1

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

             with torch.no_grad():
                lowerCAmelCase_:       Any         =  pt_model(**lowerCamelCase__   ).to_tuple()

             lowerCAmelCase_:       Union[str, Any]         =  fx_model(**lowerCamelCase__   ).to_tuple()
             self.assertEqual(len(lowerCamelCase__   )					,	len(lowerCamelCase__   )					,	"Output lengths differ between Flax and PyTorch"   )
             for fx_output, pt_output in zip(lowerCamelCase__					,	lowerCamelCase__   ):
                self.assert_almost_equals(fx_output[:, -1]					,	pt_output[:, -1].numpy()					,	4E-2   )

             with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(lowerCamelCase__   )
                lowerCAmelCase_:       str         =  pt_model_class.from_pretrained(lowerCamelCase__					,	from_flax=lowerCamelCase__   )

             with torch.no_grad():
                lowerCAmelCase_:       int         =  pt_model_loaded(**lowerCamelCase__   ).to_tuple()

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




    @slow
    def     _a						(						self   ):
       for model_class_name in self.all_model_classes:
          lowerCAmelCase_:       Tuple         =  model_class_name.from_pretrained("gpt2"					,	from_pt=lowerCamelCase__   )
          lowerCAmelCase_:       int         =  model(np.ones((1, 1)   )   )
          self.assertIsNotNone(lowerCamelCase__   )