




import inspect
import unittest

from transformers import EfficientNetConfig
from transformers.testing_utils import is_pipeline_test, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
 import torch

 from transformers import EfficientNetForImageClassification, EfficientNetModel
 from transformers.models.efficientnet.modeling_efficientnet import EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import AutoImageProcessor

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Tuple=13       ,				snake_case    :       List[str]=32       ,				snake_case    :       str=3       ,				snake_case    :       Dict=[3, 3, 5]       ,				snake_case    :       Dict=[32, 16, 24]       ,				snake_case    :       Optional[int]=[16, 24, 40]       ,				snake_case    :       str=[1, 1, 2]       ,				snake_case    :       Tuple=[1, 1, 2]       ,				snake_case    :       List[Any]=[1, 6, 6]       ,				snake_case    :       int=True       ,				snake_case    :       Union[str, Any]=True       ,				snake_case    :       Dict=37       ,				snake_case    :       Tuple="gelu"       ,				snake_case    :       Optional[int]=10       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  kernel_sizes
          __UpperCamelCase     						=  in_channels
          __UpperCamelCase     						=  out_channels
          __UpperCamelCase     						=  strides
          __UpperCamelCase     						=  num_block_repeats
          __UpperCamelCase     						=  expand_ratios
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  num_labels
          __UpperCamelCase     						=  use_labels







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

          __UpperCamelCase     						=  None
          if self.use_labels:
              __UpperCamelCase     						=  ids_tensor([self.batch_size]       ,				self.num_labels )

          __UpperCamelCase     						=  self.get_config()
          return config, pixel_values, labels







      def 					snake_case		(   self    :       Optional[int] ):
          return EfficientNetConfig(
              num_channels=self.num_channels       ,				kernel_sizes=self.kernel_sizes       ,				in_channels=self.in_channels       ,				out_channels=self.out_channels       ,				strides=self.strides       ,				num_block_repeats=self.num_block_repeats       ,				expand_ratios=self.expand_ratios       ,				hidden_act=self.hidden_act       ,				num_labels=self.num_labels       ,				)







      def 					snake_case		(   self    :       Tuple       ,				snake_case    :       Optional[int]       ,				snake_case    :       Dict       ,				snake_case    :       Union[str, Any] ):
          __UpperCamelCase     						=  EfficientNetModel(config=snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case )
          # expected last hidden states: B, C, H // 4, W // 4
          self.parent.assertEqual(
              result.last_hidden_state.shape       ,				(self.batch_size, config.hidden_dim, self.image_size // 4, self.image_size // 4)       ,				)







      def 					snake_case		(   self    :       Union[str, Any]       ,				snake_case    :       List[str]       ,				snake_case    :       List[str]       ,				snake_case    :       List[Any] ):
          __UpperCamelCase     						=  EfficientNetForImageClassification(snake_case )
          model.to(snake_case )
          model.eval()
          __UpperCamelCase     						=  model(snake_case       ,				labels=snake_case )
          self.parent.assertEqual(result.logits.shape       ,				(self.batch_size, self.num_labels) )







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''pixel_values''': pixel_values}
          return config, inputs_dict







@require_torch
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       List[str]			=    (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
      lowerCAmelCase__	:       int			=    (
          {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCAmelCase__	:       int			=    False
      lowerCAmelCase__	:       Any			=    False
      lowerCAmelCase__	:       Tuple			=    False
      lowerCAmelCase__	:       List[Any]			=    False
      lowerCAmelCase__	:       Tuple			=    False







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase     						=  EfficientNetModelTester(self )
          __UpperCamelCase     						=  ConfigTester(
              self       ,				config_class=snake_case       ,				has_text_modality=snake_case       ,				hidden_size=37 )







      def 					snake_case		(   self    :       Tuple ):
          self.create_and_test_config_common_properties()
          self.config_tester.create_and_test_config_to_json_string()
          self.config_tester.create_and_test_config_to_json_file()
          self.config_tester.create_and_test_config_from_and_save_pretrained()
          self.config_tester.create_and_test_config_with_num_labels()
          self.config_tester.check_config_can_be_init_without_params()
          self.config_tester.check_config_arguments_init()







      def 					snake_case		(   self    :       Any ):
          return







      @unittest.skip(reason='''EfficientNet does not use inputs_embeds''' )
      def 					snake_case		(   self    :       Union[str, Any] ):
          pass







      @unittest.skip(reason='''EfficientNet does not support input and output embeddings''' )
      def 					snake_case		(   self    :       Tuple ):
          pass







      @unittest.skip(reason='''EfficientNet does not use feedforward chunking''' )
      def 					snake_case		(   self    :       Optional[int] ):
          pass







      def 					snake_case		(   self    :       List[str] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs_for_common()

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

              __UpperCamelCase     						=  ['''pixel_values''']
              self.assertListEqual(arg_names[:1]       ,				snake_case )







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*snake_case )







      def 					snake_case		(   self    :       Union[str, Any] ):
          def check_hidden_states_output(snake_case    :       int       ,				snake_case    :       int       ,				snake_case    :       str ):
              __UpperCamelCase     						=  model_class(snake_case )
              model.to(snake_case )
              model.eval()

              with torch.no_grad():
                  __UpperCamelCase     						=  model(**self._prepare_for_class(snake_case       ,				snake_case ) )

              __UpperCamelCase     						=  outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
              __UpperCamelCase     						=  sum(config.num_block_repeats ) * 4
              self.assertEqual(len(snake_case )       ,				snake_case )

              # EfficientNet'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 // 2, self.model_tester.image_size // 2]       ,				)

          __UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              __UpperCamelCase     						=  True
              check_hidden_states_output(snake_case       ,				snake_case       ,				snake_case )

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

              check_hidden_states_output(snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       str ):
          __UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_image_classification(*snake_case )







      @unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
      def 					snake_case		(   self    :       List[str] ):
          pass







      @slow
      def 					snake_case		(   self    :       Dict ):
          for model_name in EFFICIENTNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              __UpperCamelCase     						=  EfficientNetModel.from_pretrained(snake_case )
              self.assertIsNotNone(snake_case )







      @is_pipeline_test
      @require_vision
      @slow
      def 					snake_case		(   self    :       Tuple ):
          super().test_pipeline_image_classification()







def     __SCREAMING_SNAKE_CASE      (     )				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''					)
    return image


@require_torch
@require_vision
class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      @cached_property
      def 					snake_case		(   self    :       Tuple ):
          return AutoImageProcessor.from_pretrained('''google/efficientnet-b7''' ) if is_vision_available() else None







      @slow
      def 					snake_case		(   self    :       Any ):
          __UpperCamelCase     						=  EfficientNetForImageClassification.from_pretrained('''google/efficientnet-b7''' ).to(snake_case )

          __UpperCamelCase     						=  self.default_image_processor
          __UpperCamelCase     						=  prepare_img()
          __UpperCamelCase     						=  image_processor(images=snake_case       ,				return_tensors='''pt''' ).to(snake_case )

          # forward pass
          with torch.no_grad():
              __UpperCamelCase     						=  model(**snake_case )

          # verify the logits
          __UpperCamelCase     						=  torch.Size((1, 1000) )
          self.assertEqual(outputs.logits.shape       ,				snake_case )

          __UpperCamelCase     						=  torch.tensor([0.00_01, 0.00_02, 0.00_02] ).to(snake_case )
          self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,				snake_case       ,				atol=1E-4 ) )












import tempfile
import unittest

import numpy as np

import transformers
from transformers import GPTaTokenizer, GPTNeoConfig, 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, 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.gpt_neo.modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel

if is_torch_available():
 import torch

class        _lowerCamelCase  :

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       Any       ,				snake_case    :       Union[str, Any]=14       ,				snake_case    :       Optional[Any]=7       ,				snake_case    :       Tuple=True       ,				snake_case    :       int=True       ,				snake_case    :       Dict=False       ,				snake_case    :       List[Any]=True       ,				snake_case    :       Optional[int]=99       ,				snake_case    :       Tuple=32       ,				snake_case    :       Any=4       ,				snake_case    :       List[Any]=4       ,				snake_case    :       int=[[["global", "local"], 2]]       ,				snake_case    :       Optional[Any]=37       ,				snake_case    :       Optional[Any]="gelu"       ,				snake_case    :       Dict=0.1       ,				snake_case    :       List[Any]=0.1       ,				snake_case    :       Union[str, Any]=512       ,				snake_case    :       int=7       ,				snake_case    :       Any=0.02       ,				):
          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  seq_length
          __UpperCamelCase     						=  is_training
          __UpperCamelCase     						=  use_input_mask
          __UpperCamelCase     						=  use_token_type_ids
          __UpperCamelCase     						=  use_labels
          __UpperCamelCase     						=  vocab_size
          __UpperCamelCase     						=  hidden_size
          __UpperCamelCase     						=  num_hidden_layers
          __UpperCamelCase     						=  num_attention_heads
          __UpperCamelCase     						=  attention_types
          __UpperCamelCase     						=  intermediate_size
          __UpperCamelCase     						=  hidden_act
          __UpperCamelCase     						=  hidden_dropout_prob
          __UpperCamelCase     						=  attention_probs_dropout_prob
          __UpperCamelCase     						=  max_position_embeddings
          __UpperCamelCase     						=  window_size
          __UpperCamelCase     						=  initializer_range
          __UpperCamelCase     						=  None
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1
          __UpperCamelCase     						=  vocab_size - 1







      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  ids_tensor([self.batch_size, self.seq_length]       ,				self.vocab_size )

          __UpperCamelCase     						=  None
          if self.use_input_mask:
              __UpperCamelCase     						=  random_attention_mask([self.batch_size, self.seq_length] )

          __UpperCamelCase     						=  GPTNeoConfig(
              vocab_size=self.vocab_size       ,				hidden_size=self.hidden_size       ,				num_layers=self.num_hidden_layers       ,				num_heads=self.num_attention_heads       ,				max_position_embeddings=self.max_position_embeddings       ,				use_cache=snake_case       ,				bos_token_id=self.bos_token_id       ,				eos_token_id=self.eos_token_id       ,				pad_token_id=self.pad_token_id       ,				window_size=self.window_size       ,				attention_types=self.attention_types       ,				)

          return (config, input_ids, input_mask)







      def 					snake_case		(   self    :       Tuple ):
          __UpperCamelCase     						=  self.prepare_config_and_inputs()
          __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  config_and_inputs
          __UpperCamelCase     						=  {'''input_ids''': input_ids, '''attention_mask''': attention_mask}
          return config, inputs_dict







      def 					snake_case		(   self    :       List[Any]       ,				snake_case    :       int       ,				snake_case    :       Union[str, Any]       ,				snake_case    :       Optional[int]       ,				snake_case    :       Optional[int] ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.ones((input_ids.shape[0], max_decoder_length)       ,				dtype='''i4''' )

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

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

          __UpperCamelCase     						=  model(snake_case )

          __UpperCamelCase     						=  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 					snake_case		(   self    :       Optional[Any]       ,				snake_case    :       Dict       ,				snake_case    :       Dict       ,				snake_case    :       Optional[Any]       ,				snake_case    :       Tuple ):
          __UpperCamelCase     						=  20
          __UpperCamelCase     						=  model_class_name(snake_case )

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

          __UpperCamelCase     						=  model.init_cache(input_ids.shape[0]       ,				snake_case )
          __UpperCamelCase     						=  jnp.broadcast_to(
              jnp.arange(input_ids.shape[-1] - 1 )[None, :]       ,				(input_ids.shape[0], input_ids.shape[-1] - 1) )

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

          __UpperCamelCase     						=  model(snake_case       ,				attention_mask=snake_case )

          __UpperCamelCase     						=  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        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       Dict			=    (FlaxGPTNeoModel, FlaxGPTNeoForCausalLM) if is_flax_available() else ()
      lowerCAmelCase__	:       Any			=    (FlaxGPTNeoForCausalLM,) if is_flax_available() else ()







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  FlaxGPTNeoModelTester(self )







      def 					snake_case		(   self    :       int ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward(snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      def 					snake_case		(   self    :       List[str] ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase						,					__UpperCamelCase						,					__UpperCamelCase     						=  self.model_tester.prepare_config_and_inputs()
              self.model_tester.check_use_cache_forward_with_attn_mask(
                  snake_case       ,				snake_case       ,				snake_case       ,				snake_case )







      @slow
      def 					snake_case		(   self    :       Union[str, Any] ):
          __UpperCamelCase     						=  GPTaTokenizer.from_pretrained('''gpt2'''       ,				pad_token='''<|endoftext|>'''       ,				padding_side='''left''' )
          __UpperCamelCase     						=  tokenizer(['''Hello this is a long string''', '''Hey''']       ,				return_tensors='''np'''       ,				padding=snake_case       ,				truncation=snake_case )

          __UpperCamelCase     						=  FlaxGPTNeoForCausalLM.from_pretrained('''EleutherAI/gpt-neo-125M''' )
          __UpperCamelCase     						=  False
          __UpperCamelCase     						=  model.config.eos_token_id

          __UpperCamelCase     						=  jax.jit(model.generate )

          __UpperCamelCase     						=  jit_generate(
              inputs['''input_ids''']       ,				attention_mask=inputs['''attention_mask''']       ,				pad_token_id=tokenizer.pad_token_id ).sequences

          __UpperCamelCase     						=  tokenizer.batch_decode(snake_case       ,				skip_special_tokens=snake_case )

          __UpperCamelCase     						=  [
              '''Hello this is a long string of text.\n\nI\'m trying to get the text of the''',
              '''Hey, I\'m a little late to the party. I\'m going to''',
          ]

          self.assertListEqual(snake_case       ,				snake_case )







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  convert_pytorch_state_dict_to_flax(pt_model.state_dict()       ,				snake_case )
                  __UpperCamelCase     						=  fx_state

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

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

                  with tempfile.TemporaryDirectory() as tmpdirname:
                      pt_model.save_pretrained(snake_case )
                      __UpperCamelCase     						=  model_class.from_pretrained(snake_case       ,				from_pt=snake_case )

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







      @is_pt_flax_cross_test
      def 					snake_case		(   self    :       Optional[Any] ):
          __UpperCamelCase						,					__UpperCamelCase     						=  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
                  __UpperCamelCase     						=  self._prepare_for_class(snake_case       ,				snake_case )
                  __UpperCamelCase     						=  {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}

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

                  __UpperCamelCase     						=  pt_model_class(snake_case ).eval()
                  __UpperCamelCase     						=  model_class(snake_case       ,				dtype=jnp.floataa )

                  __UpperCamelCase     						=  load_flax_weights_in_pytorch_model(snake_case       ,				fx_model.params )
                  __UpperCamelCase						,					__UpperCamelCase     						=  pt_inputs['''input_ids'''].shape
                  __UpperCamelCase     						=  np.random.randint(0       ,				seq_length - 1       ,				size=(batch_size,) )
                  for batch_idx, start_index in enumerate(snake_case ):
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1
                      __UpperCamelCase     						=  0
                      __UpperCamelCase     						=  1

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

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

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

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

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

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







      @slow
      def 					snake_case		(   self    :       Tuple ):
          for model_class_name in self.all_model_classes:
              __UpperCamelCase     						=  model_class_name.from_pretrained('''EleutherAI/gpt-neo-125M''' )
              __UpperCamelCase     						=  model(np.ones((1, 1) ) )
              self.assertIsNotNone(snake_case )






