




import unittest

from transformers import SpeechaTextaConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
	import torch

	from transformers.models.speech_to_text_a.modeling_speech_to_text_a import (
	    SpeechaTextaDecoder,
	    SpeechaTextaForCausalLM,
	)

@require_torch
class 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		List[Any],     _lowercase:		List[Any]=99,     _lowercase:		str=13,     _lowercase:		Union[str, Any]=16,     _lowercase:		List[str]=7,     _lowercase:		str=True,     _lowercase:		Dict=True,     _lowercase:		Optional[int]=True,     _lowercase:		Optional[Any]=False,     _lowercase:		int=True,     _lowercase:		Union[str, Any]=2,     _lowercase:		List[str]=32,     _lowercase:		Optional[Any]=4,     _lowercase:		List[str]=4,     _lowercase:		Optional[int]=30,     _lowercase:		Any=0,     _lowercase:		Optional[int]=1,     _lowercase:		List[str]=2,     _lowercase:		Union[str, Any]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  decoder_seq_length
									# For common tests
									__lowerCAmelCase					=  self.decoder_seq_length
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_attention_mask
									__lowerCAmelCase					=  use_labels

									__lowerCAmelCase					=  vocab_size
									__lowerCAmelCase					=  d_model
									__lowerCAmelCase					=  d_model
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_layers
									__lowerCAmelCase					=  decoder_ffn_dim
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  decoder_attention_heads
									__lowerCAmelCase					=  eos_token_id
									__lowerCAmelCase					=  bos_token_id
									__lowerCAmelCase					=  pad_token_id
									__lowerCAmelCase					=  decoder_start_token_id
									__lowerCAmelCase					=  use_cache
									__lowerCAmelCase					=  max_position_embeddings

									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  decoder_seq_length
									__lowerCAmelCase					=  2
									__lowerCAmelCase					=  1




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  ids_tensor([self.batch_size, self.decoder_seq_length],     self.vocab_size)

									__lowerCAmelCase					=  None
									if self.use_attention_mask:
													__lowerCAmelCase					=  ids_tensor([self.batch_size, self.decoder_seq_length],     vocab_size=2)

									__lowerCAmelCase					=  None
									if self.use_labels:
													__lowerCAmelCase					=  ids_tensor([self.batch_size, self.decoder_seq_length],     self.vocab_size)

									__lowerCAmelCase					=  SpeechaTextaConfig(
									    vocab_size=self.vocab_size,     d_model=self.d_model,     decoder_layers=self.decoder_layers,     decoder_ffn_dim=self.decoder_ffn_dim,     decoder_attention_heads=self.decoder_attention_heads,     eos_token_id=self.eos_token_id,     bos_token_id=self.bos_token_id,     use_cache=self.use_cache,     pad_token_id=self.pad_token_id,     decoder_start_token_id=self.decoder_start_token_id,     max_position_embeddings=self.max_position_embeddings,     )

									return (
									    config,
									    input_ids,
									    attention_mask,
									    lm_labels,
									)




					def     _lowercase						(   self:		Dict,     _lowercase:		int,     _lowercase:		Union[str, Any],     _lowercase:		Tuple,     _lowercase:		Any,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  True
									__lowerCAmelCase					=  SpeechaTextaDecoder(config=_lowercase).to(_lowercase).eval()
									__lowerCAmelCase					=  input_ids[:2]

									input_ids[input_ids == 0] += 1
									# first forward pass
									__lowerCAmelCase					=  model(_lowercase,     use_cache=_lowercase)
									__lowerCAmelCase					=  model(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     use_cache=_lowercase)

									self.parent.assertTrue(len(_lowercase) == len(_lowercase))
									self.parent.assertTrue(len(_lowercase) == len(_lowercase) + 1)

									__lowerCAmelCase					=  outputs["""past_key_values"""]

									# create hypothetical next token and extent to next_input_ids
									__lowerCAmelCase					=  ids_tensor((2, 1),     config.vocab_size - 1) + 1

									# append to next input_ids and
									__lowerCAmelCase					=  torch.cat([input_ids, next_tokens],     dim=-1)

									print(_lowercase)

									__lowerCAmelCase					=  model(_lowercase)["""last_hidden_state"""]
									__lowerCAmelCase					=  model(_lowercase,     past_key_values=_lowercase)["""last_hidden_state"""]

									# select random slice
									__lowerCAmelCase					=  ids_tensor((1,),     output_from_past.shape[-1]).item()
									__lowerCAmelCase					=  output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
									__lowerCAmelCase					=  output_from_past[:, 0, random_slice_idx].detach()

									# test that outputs are equal for slice
									assert torch.allclose(_lowercase,     _lowercase,     atol=1e-3)







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									(
									    (
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				(
									    __lowerCAmelCase
									)	,				
									)					=  config_and_inputs

									__lowerCAmelCase					=  {
									    """input_ids""": input_ids,
									    """attention_mask""": attention_mask,
									}
									return config, inputs_dict





@require_torch
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (SpeechaTextaDecoder, SpeechaTextaForCausalLM) if is_torch_available() else ()
					__UpperCamelCase     	= (SpeechaTextaForCausalLM,) if is_torch_available() else ()
					__UpperCamelCase     	= {"text-generation": SpeechaTextaForCausalLM} if is_torch_available() else {}
					__UpperCamelCase     	= True
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Union[str, Any],     ):


									'''simple docstring'''


									__lowerCAmelCase					=  SpeechaTextaStandaloneDecoderModelTester(self,     is_training=_lowercase)
									__lowerCAmelCase					=  ConfigTester(self,     config_class=_lowercase)




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		str):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									self.config_tester.run_common_tests()




					def     _lowercase						(   self:		Optional[int]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_decoder_model_past(*_lowercase)







					def     _lowercase						(   self:		str):


									'''simple docstring'''


									return











from __future__ import annotations

import inspect
import unittest
from typing import List, Tuple

from transformers import ConvNextConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
	import tensorflow as tf

	from transformers import TFConvNextForImageClassification, TFConvNextModel


if is_vision_available():
	from PIL import Image

	from transformers import ConvNextImageProcessor

class 		lowercase_       :




					def __init__(   self:		Any,     _lowercase:		Tuple,     _lowercase:		int=13,     _lowercase:		Dict=32,     _lowercase:		Any=3,     _lowercase:		int=4,     _lowercase:		Optional[Any]=[10, 20, 30, 40],     _lowercase:		Any=[2, 2, 3, 2],     _lowercase:		Optional[int]=True,     _lowercase:		List[Any]=True,     _lowercase:		List[Any]=37,     _lowercase:		List[str]="gelu",     _lowercase:		List[str]=10,     _lowercase:		Any=0.02,     _lowercase:		int=3,     _lowercase:		List[str]=None,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  parent
									__lowerCAmelCase					=  batch_size
									__lowerCAmelCase					=  image_size
									__lowerCAmelCase					=  num_channels
									__lowerCAmelCase					=  num_stages
									__lowerCAmelCase					=  hidden_sizes
									__lowerCAmelCase					=  depths
									__lowerCAmelCase					=  is_training
									__lowerCAmelCase					=  use_labels
									__lowerCAmelCase					=  intermediate_size
									__lowerCAmelCase					=  hidden_act
									__lowerCAmelCase					=  type_sequence_label_size
									__lowerCAmelCase					=  initializer_range
									__lowerCAmelCase					=  scope




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									__lowerCAmelCase					=  None
									if self.use_labels:
													__lowerCAmelCase					=  ids_tensor([self.batch_size],     self.type_sequence_label_size)

									__lowerCAmelCase					=  self.get_config()

									return config, pixel_values, labels




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextConfig(
									    num_channels=self.num_channels,     hidden_sizes=self.hidden_sizes,     depths=self.depths,     num_stages=self.num_stages,     hidden_act=self.hidden_act,     is_decoder=_lowercase,     initializer_range=self.initializer_range,     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		List[Any],     _lowercase:		Optional[Any],     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel(config=_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     training=_lowercase)
									# expected last hidden states: B, C, H // 32, W // 32
									self.parent.assertEqual(
									    result.last_hidden_state.shape,     (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32),     )




					def     _lowercase						(   self:		Optional[Any],     _lowercase:		int,     _lowercase:		int,     _lowercase:		int):


									'''simple docstring'''


									__lowerCAmelCase					=  self.type_sequence_label_size
									__lowerCAmelCase					=  TFConvNextForImageClassification(_lowercase)
									__lowerCAmelCase					=  model(_lowercase,     labels=_lowercase,     training=_lowercase)
									self.parent.assertEqual(result.logits.shape,     (self.batch_size, self.type_sequence_label_size))







					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.prepare_config_and_inputs()
									__lowerCAmelCase	,				__lowerCAmelCase	,				__lowerCAmelCase					=  config_and_inputs
									__lowerCAmelCase					=  {"""pixel_values""": pixel_values}
									return config, inputs_dict





@require_tf
class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__						,							unittest.TestCase				):
					__UpperCamelCase     	= (TFConvNextModel, TFConvNextForImageClassification) if is_tf_available() else ()
					__UpperCamelCase     	= (
					    {"feature-extraction": TFConvNextModel, "image-classification": TFConvNextForImageClassification}
					    if is_tf_available()
					    else {}
					)

					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False
					__UpperCamelCase     	= False




					def     _lowercase						(   self:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModelTester(self)
									__lowerCAmelCase					=  ConfigTester(
									    self,     config_class=_lowercase,     has_text_modality=_lowercase,     hidden_size=37,     )




					@unittest.skip(reason="""ConvNext does not use inputs_embeds""")
					def     _lowercase						(   self:		Any):


									'''simple docstring'''


									pass




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					@slow
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									super().test_keras_fit()




					@unittest.skip(reason="""ConvNext does not support input and output embeddings""")
					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									pass




					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)
													__lowerCAmelCase					=  inspect.signature(model.call)
													# signature.parameters is an OrderedDict => so arg_names order is deterministic
													__lowerCAmelCase					=  [*signature.parameters.keys()]

													__lowerCAmelCase					=  ["""pixel_values"""]
													self.assertListEqual(arg_names[:1],     _lowercase)




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)




					@unittest.skipIf(
					    not is_tf_available() or len(tf.config.list_physical_devices("""GPU""")) == 0,     reason="""TF does not support backprop for grouped convolutions on CPU.""",     )
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									super().test_dataset_conversion()




					def     _lowercase						(   self:		int):


									'''simple docstring'''


									def check_hidden_states_output(_lowercase:		Any,     _lowercase:		Dict,     _lowercase:		Any):
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  model(**self._prepare_for_class(_lowercase,     _lowercase))
													__lowerCAmelCase					=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

													__lowerCAmelCase					=  self.model_tester.num_stages
													self.assertEqual(len(_lowercase),     expected_num_stages + 1)

													# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
													self.assertListEqual(
													    list(hidden_states[0].shape[-2:]),     [self.model_tester.image_size // 4, self.model_tester.image_size // 4],     )

									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  True
													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)

													# check that output_hidden_states also work using config
													del inputs_dict["output_hidden_states"]
													__lowerCAmelCase					=  True

													check_hidden_states_output(_lowercase,     _lowercase,     _lowercase)




					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									__lowerCAmelCase	,				__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs_for_common()

									def check_equivalence(_lowercase:		Tuple,     _lowercase:		Tuple,     _lowercase:		str,     _lowercase:		Dict={}):
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase)
													__lowerCAmelCase					=  model(_lowercase,     return_dict=_lowercase,     **_lowercase).to_tuple()

													def recursive_check(_lowercase:		Union[str, Any],     _lowercase:		List[str]):
																	if isinstance(_lowercase,     (List, Tuple)):
																					for tuple_iterable_value, dict_iterable_value in zip(_lowercase,     _lowercase):
																									recursive_check(_lowercase,     _lowercase)
																	elif tuple_object is None:
																					return
																	else:
																					self.assertTrue(
																					    all(tf.equal(_lowercase,     _lowercase)),     msg=(
																					        """Tuple and dict output are not equal. Difference:"""
																					        f''' {tf.math.reduce_max(tf.abs(tuple_object - dict_object))}'''
																					    ),     )

																	recursive_check(_lowercase,     _lowercase)

									for model_class in self.all_model_classes:
													__lowerCAmelCase					=  model_class(_lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase)

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})

													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													__lowerCAmelCase					=  self._prepare_for_class(_lowercase,     _lowercase,     return_labels=_lowercase)
													check_equivalence(_lowercase,     _lowercase,     _lowercase,     {"""output_hidden_states""": True})




					def     _lowercase						(   self:		List[Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)







					@slow
					def     _lowercase						(   self:		Dict):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextModel.from_pretrained("""facebook/convnext-tiny-224""")
									self.assertIsNotNone(_lowercase)





def      UpperCAmelCase  (						)     ->     Any:







				'''simple docstring'''


				__lowerCAmelCase					=  Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
				return image

@require_tf
@require_vision
class 		lowercase_       (	unittest.TestCase				):




					@cached_property
					def     _lowercase						(   self:		int):


									'''simple docstring'''


									return ConvNextImageProcessor.from_pretrained("""facebook/convnext-tiny-224""") if is_vision_available() else None







					@slow
					def     _lowercase						(   self:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  TFConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""")

									__lowerCAmelCase					=  self.default_image_processor
									__lowerCAmelCase					=  prepare_img()
									__lowerCAmelCase					=  image_processor(images=_lowercase,     return_tensors="""tf""")

									# forward pass
									__lowerCAmelCase					=  model(**_lowercase)

									# verify the logits
									__lowerCAmelCase					=  tf.TensorShape((1, 1000))
									self.assertEqual(outputs.logits.shape,     _lowercase)

									__lowerCAmelCase					=  tf.constant([-0.0_260, -0.4_739, 0.1_911])

									tf.debugging.assert_near(outputs.logits[0, :3],     _lowercase,     atol=1e-4)





