






from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
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.models.swin.modeling_tf_swin import (
					    TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
					    TFSwinForImageClassification,
					    TFSwinForMaskedImageModeling,
					    TFSwinModel,
					)


if is_vision_available():
					from PIL import Image

					from transformers import AutoImageProcessor





class        a__      :






		def __init__(      self							, lowercase__							, lowercase__=13							, lowercase__=32							, lowercase__=2							, lowercase__=3							, lowercase__=16							, lowercase__=[1, 2, 1]							, lowercase__=[2, 2, 4]							, lowercase__=2							, lowercase__=2.0							, lowercase__=True							, lowercase__=0.0							, lowercase__=0.0							, lowercase__=0.1							, lowercase__="gelu"							, lowercase__=False							, lowercase__=True							, lowercase__=0.02							, lowercase__=1e-5							, lowercase__=True							, lowercase__=None							, lowercase__=True							, lowercase__=10							, lowercase__=8							, )				->				None:
				__A    =							parent
				__A    =							batch_size
				__A    =							image_size
				__A    =							patch_size
				__A    =							num_channels
				__A    =							embed_dim
				__A    =							depths
				__A    =							num_heads
				__A    =							window_size
				__A    =							mlp_ratio
				__A    =							qkv_bias
				__A    =							hidden_dropout_prob
				__A    =							attention_probs_dropout_prob
				__A    =							drop_path_rate
				__A    =							hidden_act
				__A    =							use_absolute_embeddings
				__A    =							patch_norm
				__A    =							layer_norm_eps
				__A    =							initializer_range
				__A    =							is_training
				__A    =							scope
				__A    =							use_labels
				__A    =							type_sequence_label_size
				__A    =							encoder_stride






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A    =							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

				__A    =							self.get_config()

				return config, pixel_values, labels






		def 					_lowerCamelCase					(      self						)				->				Optional[Any]:
				return SwinConfig(
				    image_size=self.image_size							, patch_size=self.patch_size							, num_channels=self.num_channels							, embed_dim=self.embed_dim							, depths=self.depths							, num_heads=self.num_heads							, window_size=self.window_size							, mlp_ratio=self.mlp_ratio							, qkv_bias=self.qkv_bias							, hidden_dropout_prob=self.hidden_dropout_prob							, attention_probs_dropout_prob=self.attention_probs_dropout_prob							, drop_path_rate=self.drop_path_rate							, hidden_act=self.hidden_act							, use_absolute_embeddings=self.use_absolute_embeddings							, path_norm=self.patch_norm							, layer_norm_eps=self.layer_norm_eps							, initializer_range=self.initializer_range							, encoder_stride=self.encoder_stride							, )






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				Tuple:
				__A    =							TFSwinModel(config=lowercase__						)
				__A    =							model(lowercase__						)

				__A    =							((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
				__A    =							int(config.embed_dim * 2 ** (len(config.depths						) - 1)						)

				self.parent.assertEqual(result.last_hidden_state.shape							, (self.batch_size, expected_seq_len, expected_dim)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							TFSwinForMaskedImageModeling(config=lowercase__						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(
				    result.logits.shape							, (self.batch_size, self.num_channels, self.image_size, self.image_size)						)

				# test greyscale images
				__A    =							1
				__A    =							TFSwinForMaskedImageModeling(lowercase__						)

				__A    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, 1, self.image_size, self.image_size)						)






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__						)				->				List[str]:
				__A    =							self.type_sequence_label_size
				__A    =							TFSwinForImageClassification(lowercase__						)
				__A    =							model(lowercase__							, labels=lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)

				# test greyscale images
				__A    =							1
				__A    =							TFSwinForImageClassification(lowercase__						)
				__A    =							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]						)
				__A    =							model(lowercase__						)
				self.parent.assertEqual(result.logits.shape							, (self.batch_size, self.type_sequence_label_size)						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							self.prepare_config_and_inputs()
				__A ,   __A ,   __A    =							config_and_inputs
				__A    =							{"pixel_values": pixel_values}
				return config, inputs_dict





@require_tf
class        a__      (	lowerCAmelCase__	,       lowerCAmelCase__	,       unittest.TestCase		):
		UpperCAmelCase_							:       Tuple 					= (
		    (
		        TFSwinModel,
		        TFSwinForImageClassification,
		        TFSwinForMaskedImageModeling,
		    )
		    if is_tf_available()
		    else ()
		)
		UpperCAmelCase_							:       Tuple 					= (
		    {'feature-extraction': TFSwinModel, 'image-classification': TFSwinForImageClassification}
		    if is_tf_available()
		    else {}
		)

		UpperCAmelCase_							:       int 					= False
		UpperCAmelCase_							:       str 					= False
		UpperCAmelCase_							:       Any 					= False
		UpperCAmelCase_							:       Optional[Any] 					= False






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							TFSwinModelTester(self						)
				__A    =							ConfigTester(self							, config_class=lowercase__							, embed_dim=37						)






		def 					_lowerCamelCase					(      self						)				->				Optional[int]:
				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 					_lowerCamelCase					(      self						)				->				Any:
				return






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_masked_image_modeling(*lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Tuple:
				__A    =							self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*lowercase__						)






		@unittest.skip(reason="Swin does not use inputs_embeds"						)
		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				pass






		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes:
						__A    =							model_class(lowercase__						)
						self.assertIsInstance(model.get_input_embeddings()							, tf.keras.layers.Layer						)
						__A    =							model.get_output_embeddings()
						self.assertTrue(x is None or isinstance(lowercase__							, tf.keras.layers.Dense						)						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

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

						__A    =							["pixel_values"]
						self.assertListEqual(arg_names[:1]							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							True

				for model_class in self.all_model_classes:
						__A    =							True
						__A    =							False
						__A    =							True
						__A    =							model_class(lowercase__						)
						__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.attentions
						__A    =							len(self.model_tester.depths						)
						self.assertEqual(len(lowercase__						)							, lowercase__						)

						# check that output_attentions also work using config
						del inputs_dict["output_attentions"]
						__A    =							True
						__A    =							config.window_size**2
						__A    =							model_class(lowercase__						)
						__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
						__A    =							outputs.attentions
						self.assertEqual(len(lowercase__						)							, lowercase__						)

						self.assertListEqual(
						    list(attentions[0].shape[-3:]						)							, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							, )
						__A    =							len(lowercase__						)

						# Check attention is always last and order is fine
						__A    =							True
						__A    =							True
						__A    =							model_class(lowercase__						)
						__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)

						if hasattr(self.model_tester							, "num_hidden_states_types"						):
								__A    =							self.model_tester.num_hidden_states_types
						else:
								# also another +1 for reshaped_hidden_states
								__A    =							2
						self.assertEqual(out_len + added_hidden_states							, len(lowercase__						)						)

						__A    =							outputs.attentions

						self.assertEqual(len(lowercase__						)							, lowercase__						)

						self.assertListEqual(
						    list(self_attentions[0].shape[-3:]						)							, [self.model_tester.num_heads[0], window_size_squared, window_size_squared]							, )






		def 					_lowerCamelCase					(      self							, lowercase__							, lowercase__							, lowercase__							, lowercase__						)				->				Optional[int]:
				__A    =							model_class(lowercase__						)
				__A    =							model(**self._prepare_for_class(lowercase__							, lowercase__						)						)
				__A    =							outputs.hidden_states

				__A    =							getattr(
				    self.model_tester							, "expected_num_hidden_layers"							, len(self.model_tester.depths						) + 1						)
				self.assertEqual(len(lowercase__						)							, lowercase__						)

				# Swin has a different seq_length
				__A    =							to_atuple(config.patch_size						)

				__A    =							(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

				self.assertListEqual(
				    list(hidden_states[0].shape[-2:]						)							, [num_patches, self.model_tester.embed_dim]							, )

				__A    =							outputs.reshaped_hidden_states
				self.assertEqual(len(lowercase__						)							, lowercase__						)

				__A ,   __A ,   __A ,   __A    =							reshaped_hidden_states[0].shape

				__A    =							tf.reshape(reshaped_hidden_states[0]							, (batch_size, num_channels, height * width)						)
				__A    =							tf.transpose(lowercase__							, (0, 2, 1)						)

				self.assertListEqual(
				    list(reshaped_hidden_states.shape[-2:]						)							, [num_patches, self.model_tester.embed_dim]							, )






		def 					_lowerCamelCase					(      self						)				->				Union[str, Any]:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()

				__A    =							to_atuple(self.model_tester.image_size						)

				for model_class in self.all_model_classes:
						__A    =							True
						self.check_hidden_states_output(lowercase__							, lowercase__							, lowercase__							, lowercase__						)

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

						self.check_hidden_states_output(lowercase__							, lowercase__							, lowercase__							, lowercase__						)






		def 					_lowerCamelCase					(      self						)				->				Any:
				__A ,   __A    =							self.model_tester.prepare_config_and_inputs_for_common()
				__A    =							3

				__A    =							to_atuple(self.model_tester.image_size						)
				__A    =							to_atuple(config.patch_size						)

				__A    =							image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
				__A    =							image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

				for model_class in self.all_model_classes:
						__A    =							True
						self.check_hidden_states_output(lowercase__							, lowercase__							, lowercase__							, (padded_height, padded_width)						)

						# check that output_hidden_states also work using config
						del inputs_dict["output_hidden_states"]
						__A    =							True
						self.check_hidden_states_output(lowercase__							, lowercase__							, lowercase__							, (padded_height, padded_width)						)






		@slow
		def 					_lowerCamelCase					(      self						)				->				str:
				for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						__A    =							TFSwinModel.from_pretrained(lowercase__						)
						self.assertIsNotNone(lowercase__						)





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






		@cached_property
		def 					_lowerCamelCase					(      self						)				->				int:
				return (
				    AutoImageProcessor.from_pretrained("microsoft/swin-tiny-patch4-window7-224"						)
				    if is_vision_available()
				    else None
				)






		@slow
		def 					_lowerCamelCase					(      self						)				->				List[Any]:
				__A    =							TFSwinForImageClassification.from_pretrained("microsoft/swin-tiny-patch4-window7-224"						)
				__A    =							self.default_image_processor

				__A    =							Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
				__A    =							image_processor(images=lowercase__							, return_tensors="tf"						)

				# forward pass
				__A    =							model(lowercase__						)

				# verify the logits
				__A    =							tf.TensorShape((1, 1000)						)
				self.assertEqual(outputs.logits.shape							, lowercase__						)
				__A    =							tf.constant([-0.0948, -0.6454, -0.0921]						)
				self.assertTrue(np.allclose(outputs.logits[0, :3]							, lowercase__							, atol=1e-4						)						)





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  A_ :







 """simple docstring"""





 def __init__(      self       :			Tuple							,     _a       :			Optional[int]							,     _a       :			Tuple=1_4							,     _a       :			List[Any]=7							,     _a       :			List[str]=True							,     _a       :			Tuple=True							,     _a       :			Any=True							,     _a       :			str=9_9							,     _a       :			str=3_2							,     _a       :			Optional[Any]=5							,     _a       :			int=4							,     _a       :			Union[str, Any]=3_7							,     _a       :			List[Any]="gelu"							,     _a       :			List[Any]=0.1							,     _a       :			Tuple=0.1							,     _a       :			int=5_1_2							,     _a       :			Tuple=0.0_2							,     _a       :			Dict=None							,     )     ->      Tuple:
      UpperCAmelCase    :					List[Any]			=      parent
      UpperCAmelCase    :					str			=      batch_size
      UpperCAmelCase    :					Dict			=      seq_length
      UpperCAmelCase    :					Any			=      is_training
      UpperCAmelCase    :					Union[str, Any]			=      use_input_mask
      UpperCAmelCase    :					int			=      use_labels
      UpperCAmelCase    :					Optional[Any]			=      vocab_size
      UpperCAmelCase    :					Union[str, Any]			=      d_model
      UpperCAmelCase    :					Optional[Any]			=      num_hidden_layers
      UpperCAmelCase    :					Tuple			=      num_attention_heads
      UpperCAmelCase    :					str			=      ffn_dim
      UpperCAmelCase    :					Dict			=      activation_function
      UpperCAmelCase    :					int			=      activation_dropout
      UpperCAmelCase    :					Union[str, Any]			=      attention_dropout
      UpperCAmelCase    :					Optional[int]			=      max_position_embeddings
      UpperCAmelCase    :					str			=      initializer_range
      UpperCAmelCase    :					Tuple			=      None
      UpperCAmelCase    :					List[Any]			=      0
      UpperCAmelCase    :					List[str]			=      2
      UpperCAmelCase    :					Dict			=      1
 def 	_UpperCamelCase     (      self       :			List[str]					)     ->      List[str]:
      UpperCAmelCase    :					str			=      np.clip(ids_tensor([self.batch_size, self.seq_length]							,     self.vocab_size					)							,     3							,     self.vocab_size					)

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

      UpperCAmelCase    :					Union[str, 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       :			str					)     ->      List[str]:
      UpperCAmelCase    :					Tuple			=      self.prepare_config_and_inputs()
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[Any]			=      config_and_inputs
      UpperCAmelCase    :					str			=      {"input_ids": input_ids, "attention_mask": attention_mask}
      return config, inputs_dict
 def 	_UpperCamelCase     (      self       :			Dict					)     ->      List[str]:
      UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Dict			=      self.prepare_config_and_inputs()

      UpperCAmelCase    :					List[Any]			=      floats_tensor([self.batch_size, self.seq_length, self.hidden_size]					)
      UpperCAmelCase    :					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       :			int							,     _a       :			Optional[int]							,     _a       :			Dict							,     _a       :			Tuple							,     _a       :			Dict					)     ->      str:
      UpperCAmelCase    :					List[str]			=      2_0
      UpperCAmelCase    :					Optional[Any]			=      model_class_name(_a					)

      UpperCAmelCase    :					Dict			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					List[Any]			=      jnp.ones((input_ids.shape[0], max_decoder_length)							,     dtype="i4"					)

      UpperCAmelCase    :					str			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)
      UpperCAmelCase    :					List[str]			=      model(
          input_ids[:, :-1]							,     attention_mask=_a							,     past_key_values=_a							,     position_ids=_a							,     )

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

      UpperCAmelCase    :					int			=      model(_a					)

      UpperCAmelCase    :					Optional[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       :			str							,     _a       :			List[Any]							,     _a       :			Any							,     _a       :			Optional[Any]							,     _a       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					List[Any]			=      2_0
      UpperCAmelCase    :					Any			=      model_class_name(_a					)

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

      UpperCAmelCase    :					Optional[int]			=      model.init_cache(input_ids.shape[0]							,     _a					)
      UpperCAmelCase    :					int			=      jnp.broadcast_to(
          jnp.arange(input_ids.shape[-1] - 1					)[None, :]							,     (input_ids.shape[0], input_ids.shape[-1] - 1)					)

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

      UpperCAmelCase    :					Union[str, Any]			=      model(_a							,     attention_mask=_a					)
      UpperCAmelCase    :					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  A_ (			a_   ,    a_   ,    unittest.TestCase  ):







 """simple docstring"""





 _UpperCamelCase					:			Tuple     	=       (FlaxXGLMModel, FlaxXGLMForCausalLM) if is_flax_available() else ()
 _UpperCamelCase					:			int     	=       (FlaxXGLMForCausalLM,) if is_flax_available() else ()
 def 	_UpperCamelCase     (      self       :			List[Any]					)     ->      Optional[Any]:
      UpperCAmelCase    :					Any			=      FlaxXGLMModelTester(self					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      self.model_tester.prepare_config_and_inputs()
           self.model_tester.check_use_cache_forward(_a							,     _a							,     _a							,     _a					)
 def 	_UpperCamelCase     (      self       :			int					)     ->      Dict:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase	,			UpperCAmelCase	,			UpperCAmelCase    :					Tuple			=      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       :			int					)     ->      Dict:
      UpperCAmelCase    :					int			=      XGLMTokenizer.from_pretrained("XGLM"							,     padding_side="left"					)
      UpperCAmelCase    :					Optional[int]			=      tokenizer(["Hello this is a long string", "Hey"]							,     return_tensors="np"							,     padding=_a							,     truncation=_a					)

      UpperCAmelCase    :					int			=      FlaxXGLMForCausalLM.from_pretrained("facebook/xglm-564M"					)
      UpperCAmelCase    :					Optional[Any]			=      1
      UpperCAmelCase    :					Tuple			=      False

      UpperCAmelCase    :					Any			=      jax.jit(model.generate					)

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

      UpperCAmelCase    :					Any			=      tokenizer.batch_decode(_a							,     skip_special_tokens=_a					)

      UpperCAmelCase    :					Union[str, Any]			=      [
          "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       :			Union[str, Any]					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[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
                UpperCAmelCase    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					int			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

                # load corresponding PyTorch class
                UpperCAmelCase    :					int			=      model_class.__name__[4:]  # Skip the "Flax" at the beginning
                UpperCAmelCase    :					str			=      getattr(_a							,     _a					)

                UpperCAmelCase	,			UpperCAmelCase    :					List[str]			=      pt_inputs["input_ids"].shape
                UpperCAmelCase    :					str			=      np.random.randint(0							,     seq_length - 1							,     size=(batch_size,)					)
                for batch_idx, start_index in enumerate(_a					):
                     UpperCAmelCase    :					str			=      0
                     UpperCAmelCase    :					Optional[Any]			=      1
                     UpperCAmelCase    :					int			=      0
                     UpperCAmelCase    :					Union[str, Any]			=      1
                UpperCAmelCase    :					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.
                UpperCAmelCase    :					List[Any]			=      False
                UpperCAmelCase    :					Union[str, Any]			=      model_class(_a							,     dtype=jnp.floataa					)
                UpperCAmelCase    :					Union[str, Any]			=      convert_pytorch_state_dict_to_flax(pt_model.state_dict()							,     _a					)
                UpperCAmelCase    :					List[Any]			=      fx_state

                with torch.no_grad():
                     UpperCAmelCase    :					str			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					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					)
                     UpperCAmelCase    :					Optional[Any]			=      model_class.from_pretrained(_a							,     from_pt=_a					)

                UpperCAmelCase    :					Optional[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       :			int					)     ->      str:
      UpperCAmelCase	,			UpperCAmelCase    :					Optional[int]			=      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    :					str			=      self._prepare_for_class(_a							,     _a					)
                UpperCAmelCase    :					Optional[Any]			=      {k: torch.tensor(v.tolist()					) for k, v in prepared_inputs_dict.items()}

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

                UpperCAmelCase    :					Optional[int]			=      pt_model_class(_a					).eval()
                UpperCAmelCase    :					int			=      False
                UpperCAmelCase    :					int			=      model_class(_a							,     dtype=jnp.floataa					)

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

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

                with torch.no_grad():
                     UpperCAmelCase    :					int			=      pt_model(**_a					).to_tuple()

                UpperCAmelCase    :					Optional[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					)
                     UpperCAmelCase    :					List[Any]			=      pt_model_class.from_pretrained(_a							,     from_flax=_a					)

                with torch.no_grad():
                     UpperCAmelCase    :					List[Any]			=      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       :			Any					)     ->      str:
      for model_class_name in self.all_model_classes:
           UpperCAmelCase    :					Dict			=      model_class_name.from_pretrained("facebook/xglm-564M"					)
           UpperCAmelCase    :					List[Any]			=      model(np.ones((1, 1)					)					)
           self.assertIsNotNone(_a					)



