





'''simple docstring'''






import unittest

from transformers import XLMConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

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


if is_torch_available():
	import torch

	from transformers import (
	    XLMForMultipleChoice,
	    XLMForQuestionAnswering,
	    XLMForQuestionAnsweringSimple,
	    XLMForSequenceClassification,
	    XLMForTokenClassification,
	    XLMModel,
	    XLMWithLMHeadModel,
	)
	from transformers.models.xlm.modeling_xlm import XLM_PRETRAINED_MODEL_ARCHIVE_LIST




class  _A :

		def __init__(   self					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[int]=13  ,      __magic_name__					:   Any=7  ,      __magic_name__					:   Any=True  ,      __magic_name__					:   Optional[int]=True  ,      __magic_name__					:   Any=True  ,      __magic_name__					:   str=True  ,      __magic_name__					:   Tuple=True  ,      __magic_name__					:   str=False  ,      __magic_name__					:   List[Any]=False  ,      __magic_name__					:   Optional[Any]=False  ,      __magic_name__					:   int=2  ,      __magic_name__					:   Any=99  ,      __magic_name__					:   Union[str, Any]=0  ,      __magic_name__					:   Tuple=32  ,      __magic_name__					:   Optional[int]=5  ,      __magic_name__					:   Tuple=4  ,      __magic_name__					:   int=0.1  ,      __magic_name__					:   Optional[Any]=0.1  ,      __magic_name__					:   int=5_12  ,      __magic_name__					:   str=2  ,      __magic_name__					:   Tuple=0.02  ,      __magic_name__					:   List[str]=2  ,      __magic_name__					:   List[Any]=4  ,      __magic_name__					:   str="last"  ,      __magic_name__					:   Dict=True  ,      __magic_name__					:   Optional[int]=None  ,      __magic_name__					:   int=0  ,      )					->    str:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     parent
					__snake_case					:      List[str]							      =     batch_size
					__snake_case					:      List[str]							      =     seq_length
					__snake_case					:      Dict							      =     is_training
					__snake_case					:      Optional[Any]							      =     use_input_lengths
					__snake_case					:      Optional[int]							      =     use_token_type_ids
					__snake_case					:      int							      =     use_labels
					__snake_case					:      Any							      =     gelu_activation
					__snake_case					:      List[str]							      =     sinusoidal_embeddings
					__snake_case					:      int							      =     causal
					__snake_case					:      int							      =     asm
					__snake_case					:      Any							      =     n_langs
					__snake_case					:      Any							      =     vocab_size
					__snake_case					:      str							      =     n_special
					__snake_case					:      Union[str, Any]							      =     hidden_size
					__snake_case					:      Optional[Any]							      =     num_hidden_layers
					__snake_case					:      int							      =     num_attention_heads
					__snake_case					:      Any							      =     hidden_dropout_prob
					__snake_case					:      Any							      =     attention_probs_dropout_prob
					__snake_case					:      Optional[int]							      =     max_position_embeddings
					__snake_case					:      str							      =     type_sequence_label_size
					__snake_case					:      Optional[int]							      =     initializer_range
					__snake_case					:      List[str]							      =     num_labels
					__snake_case					:      List[str]							      =     num_choices
					__snake_case					:      Tuple							      =     summary_type
					__snake_case					:      List[str]							      =     use_proj
					__snake_case					:      Dict							      =     scope
					__snake_case					:      Any							      =     bos_token_id

		def 	lowercase__     (   self					:   List[str]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.vocab_size			)
					__snake_case					:      List[Any]							      =     random_attention_mask([self.batch_size, self.seq_length]			)

					__snake_case					:      List[Any]							      =     None
					if self.use_input_lengths:
								__snake_case					:      Optional[int]							      =     (
								    ids_tensor([self.batch_size]  ,      vocab_size=2			) + self.seq_length - 2
								)  # small variation of seq_length

					__snake_case					:      Optional[Any]							      =     None
					if self.use_token_type_ids:
								__snake_case					:      int							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.n_langs			)

					__snake_case					:      List[str]							      =     None
					__snake_case					:      List[Any]							      =     None
					__snake_case					:      List[Any]							      =     None
					if self.use_labels:
								__snake_case					:      Optional[int]							      =     ids_tensor([self.batch_size]  ,      self.type_sequence_label_size			)
								__snake_case					:      Optional[int]							      =     ids_tensor([self.batch_size, self.seq_length]  ,      self.num_labels			)
								__snake_case					:      int							      =     ids_tensor([self.batch_size]  ,      2			).float()
								__snake_case					:      List[str]							      =     ids_tensor([self.batch_size]  ,      self.num_choices			)

					__snake_case					:      Any							      =     self.get_config()

					return (
					    config,
					    input_ids,
					    token_type_ids,
					    input_lengths,
					    sequence_labels,
					    token_labels,
					    is_impossible_labels,
					    choice_labels,
					    input_mask,
					)

		def 	lowercase__     (   self					:   str			)					->    Optional[Any]:


					"""simple docstring"""

					return XLMConfig(
					    vocab_size=self.vocab_size  ,      n_special=self.n_special  ,      emb_dim=self.hidden_size  ,      n_layers=self.num_hidden_layers  ,      n_heads=self.num_attention_heads  ,      dropout=self.hidden_dropout_prob  ,      attention_dropout=self.attention_probs_dropout_prob  ,      gelu_activation=self.gelu_activation  ,      sinusoidal_embeddings=self.sinusoidal_embeddings  ,      asm=self.asm  ,      causal=self.causal  ,      n_langs=self.n_langs  ,      max_position_embeddings=self.max_position_embeddings  ,      initializer_range=self.initializer_range  ,      summary_type=self.summary_type  ,      use_proj=self.use_proj  ,      num_labels=self.num_labels  ,      bos_token_id=self.bos_token_id  ,      )

		def 	lowercase__     (   self					:   List[str]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Any  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Any  ,      )					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[Any]							      =     XLMModel(config=__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					__snake_case					:      Any							      =     model(__magic_name__  ,      lengths=__magic_name__  ,      langs=__magic_name__			)
					__snake_case					:      List[str]							      =     model(__magic_name__  ,      langs=__magic_name__			)
					__snake_case					:      Optional[int]							      =     model(__magic_name__			)
					self.parent.assertEqual(result.last_hidden_state.shape  ,      (self.batch_size, self.seq_length, self.hidden_size)			)

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   int  ,      __magic_name__					:   str  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[int]  ,      )					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     XLMWithLMHeadModel(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					__snake_case					:      Any							      =     model(__magic_name__  ,      token_type_ids=__magic_name__  ,      labels=__magic_name__			)
					self.parent.assertEqual(result.loss.shape  ,      ()			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.vocab_size)			)

		def 	lowercase__     (   self					:   Dict  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Dict  ,      __magic_name__					:   int  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Union[str, Any]  ,      )					->    List[str]:


					"""simple docstring"""

					__snake_case					:      int							      =     XLMForQuestionAnsweringSimple(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					__snake_case					:      int							      =     model(__magic_name__			)

					__snake_case					:      Any							      =     model(__magic_name__  ,      start_positions=__magic_name__  ,      end_positions=__magic_name__			)
					__snake_case					:      Tuple							      =     outputs
					self.parent.assertEqual(result.start_logits.shape  ,      (self.batch_size, self.seq_length)			)
					self.parent.assertEqual(result.end_logits.shape  ,      (self.batch_size, self.seq_length)			)

		def 	lowercase__     (   self					:   int  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Dict  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Tuple  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      Dict							      =     XLMForQuestionAnswering(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					__snake_case					:      Optional[Any]							      =     model(__magic_name__			)

					__snake_case					:      Optional[Any]							      =     model(
					    __magic_name__  ,      start_positions=__magic_name__  ,      end_positions=__magic_name__  ,      cls_index=__magic_name__  ,      is_impossible=__magic_name__  ,      p_mask=__magic_name__  ,      )

					__snake_case					:      Dict							      =     model(
					    __magic_name__  ,      start_positions=__magic_name__  ,      end_positions=__magic_name__  ,      cls_index=__magic_name__  ,      is_impossible=__magic_name__  ,      )

					((__snake_case)      ,     )					:      Dict							      =     result_with_labels.to_tuple()

					__snake_case					:      str							      =     model(__magic_name__  ,      start_positions=__magic_name__  ,      end_positions=__magic_name__			)

					((__snake_case)      ,     )					:      List[Any]							      =     result_with_labels.to_tuple()

					self.parent.assertEqual(result_with_labels.loss.shape  ,      ()			)
					self.parent.assertEqual(result.start_top_log_probs.shape  ,      (self.batch_size, model.config.start_n_top)			)
					self.parent.assertEqual(result.start_top_index.shape  ,      (self.batch_size, model.config.start_n_top)			)
					self.parent.assertEqual(
					    result.end_top_log_probs.shape  ,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)			)
					self.parent.assertEqual(
					    result.end_top_index.shape  ,      (self.batch_size, model.config.start_n_top * model.config.end_n_top)			)
					self.parent.assertEqual(result.cls_logits.shape  ,      (self.batch_size,)			)

		def 	lowercase__     (   self					:   List[Any]  ,      __magic_name__					:   Union[str, Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   int  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Tuple  ,      )					->    int:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     XLMForSequenceClassification(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					__snake_case					:      Optional[int]							      =     model(__magic_name__			)
					__snake_case					:      Optional[int]							      =     model(__magic_name__  ,      labels=__magic_name__			)
					self.parent.assertEqual(result.loss.shape  ,      ()			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.type_sequence_label_size)			)

		def 	lowercase__     (   self					:   Union[str, Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   Any  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Any  ,      __magic_name__					:   List[str]  ,      )					->    str:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.num_labels
					__snake_case					:      Dict							      =     XLMForTokenClassification(__magic_name__			)
					model.to(__magic_name__			)
					model.eval()

					__snake_case					:      Optional[Any]							      =     model(__magic_name__  ,      attention_mask=__magic_name__  ,      labels=__magic_name__			)
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.seq_length, self.num_labels)			)

		def 	lowercase__     (   self					:   Optional[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   int  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   int  ,      __magic_name__					:   Optional[Any]  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[str]  ,      )					->    Dict:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.num_choices
					__snake_case					:      Optional[Any]							      =     XLMForMultipleChoice(config=__magic_name__			)
					model.to(__magic_name__			)
					model.eval()
					__snake_case					:      List[str]							      =     input_ids.unsqueeze(1			).expand(-1  ,      self.num_choices  ,      -1			).contiguous()
					__snake_case					:      List[str]							      =     token_type_ids.unsqueeze(1			).expand(-1  ,      self.num_choices  ,      -1			).contiguous()
					__snake_case					:      List[str]							      =     input_mask.unsqueeze(1			).expand(-1  ,      self.num_choices  ,      -1			).contiguous()
					__snake_case					:      Optional[Any]							      =     model(
					    __magic_name__  ,      attention_mask=__magic_name__  ,      token_type_ids=__magic_name__  ,      labels=__magic_name__  ,      )
					self.parent.assertEqual(result.logits.shape  ,      (self.batch_size, self.num_choices)			)

		def 	lowercase__     (   self					:   List[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.prepare_config_and_inputs()
					(
					    (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     (
					    __snake_case
					)      ,     
					)					:      Optional[Any]							      =     config_and_inputs
					__snake_case					:      Any							      =     {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """lengths""": input_lengths}
					return config, inputs_dict





@require_torch
class  _A (					__lowercase						,					__lowercase						,					__lowercase						,					unittest.TestCase		):
		lowercase__:   Optional[Any]				     =   (
		    (
		        XLMModel,
		        XLMWithLMHeadModel,
		        XLMForQuestionAnswering,
		        XLMForSequenceClassification,
		        XLMForQuestionAnsweringSimple,
		        XLMForTokenClassification,
		        XLMForMultipleChoice,
		    )
		    if is_torch_available()
		    else ()
		)
		lowercase__:   Union[str, Any]				     =   (
		    (XLMWithLMHeadModel,) if is_torch_available() else ()
		)  # TODO (PVP): Check other models whether language generation is also applicable
		lowercase__:   Union[str, Any]				     =   (
		    {
		        '''feature-extraction''': XLMModel,
		        '''fill-mask''': XLMWithLMHeadModel,
		        '''question-answering''': XLMForQuestionAnsweringSimple,
		        '''text-classification''': XLMForSequenceClassification,
		        '''text-generation''': XLMWithLMHeadModel,
		        '''token-classification''': XLMForTokenClassification,
		        '''zero-shot''': XLMForSequenceClassification,
		    }
		    if is_torch_available()
		    else {}
		)

		def 	lowercase__     (   self					:   Optional[int]  ,      __magic_name__					:   List[Any]  ,      __magic_name__					:   str  ,      __magic_name__					:   Optional[int]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Optional[Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					if (
					    pipeline_test_casse_name == "QAPipelineTests"
					    and tokenizer_name is not None
					    and not tokenizer_name.endswith("""Fast"""			)
					):
								# `QAPipelineTests` fails for a few models when the slower tokenizer are used.
								# (The slower tokenizers were never used for pipeline tests before the pipeline testing rework)
								# TODO: check (and possibly fix) the `QAPipelineTests` with slower tokenizer
								return True

					return False

		def 	lowercase__     (   self					:   Tuple  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   Union[str, Any]=False			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     super()._prepare_for_class(__magic_name__  ,      __magic_name__  ,      return_labels=__magic_name__			)

					if return_labels:
								if model_class.__name__ == "XLMForQuestionAnswering":
											__snake_case					:      List[Any]							      =     torch.zeros(
											    self.model_tester.batch_size  ,      dtype=torch.long  ,      device=__magic_name__			)
											__snake_case					:      Optional[Any]							      =     torch.zeros(
											    self.model_tester.batch_size  ,      dtype=torch.long  ,      device=__magic_name__			)

					return inputs_dict

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Any							      =     XLMModelTester(self			)
					__snake_case					:      List[str]							      =     ConfigTester(self  ,      config_class=__magic_name__  ,      emb_dim=37			)

		def 	lowercase__     (   self					:   int			)					->    Tuple:


					"""simple docstring"""

					self.config_tester.run_common_tests()

		def 	lowercase__     (   self					:   Dict			)					->    int:


					"""simple docstring"""

					__snake_case					:      Tuple							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_model(*__magic_name__			)

		def 	lowercase__     (   self					:   Any			)					->    Union[str, Any]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_lm_head(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Any							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_simple_qa(*__magic_name__			)

		def 	lowercase__     (   self					:   List[str]			)					->    List[str]:


					"""simple docstring"""

					__snake_case					:      Dict							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_qa(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[int]			)					->    str:


					"""simple docstring"""

					__snake_case					:      Optional[Any]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_sequence_classif(*__magic_name__			)

		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Dict:


					"""simple docstring"""

					__snake_case					:      List[str]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_token_classif(*__magic_name__			)

		def 	lowercase__     (   self					:   Optional[Any]			)					->    Optional[Any]:


					"""simple docstring"""

					__snake_case					:      Optional[int]							      =     self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_xlm_for_multiple_choice(*__magic_name__			)

		def 	lowercase__     (   self					:   str  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Tuple  ,      __magic_name__					:   str  ,      __magic_name__					:   int  ,      __magic_name__					:   Any  ,      __magic_name__					:   int=False  ,      __magic_name__					:   Any=1			)					->    str:


					"""simple docstring"""

					self.assertIsInstance(__magic_name__  ,      __magic_name__			)
					self.assertListEqual(
					    [isinstance(__magic_name__  ,      __magic_name__			) for iter_attentions in attentions]  ,      [True] * len(__magic_name__			)			)
					self.assertEqual(len(__magic_name__			)  ,      (max_length - min_length) * num_beam_groups			)

					for idx, iter_attentions in enumerate(__magic_name__			):
								# adds PAD dummy token
								__snake_case					:      List[str]							      =     min_length + idx + 1
								__snake_case					:      List[Any]							      =     min_length + idx + 1

								__snake_case					:      int							      =     (
								    batch_size * num_beam_groups,
								    config.num_attention_heads,
								    tgt_len,
								    src_len,
								)
								# check attn size
								self.assertListEqual(
								    [layer_attention.shape for layer_attention in iter_attentions]  ,      [expected_shape] * len(__magic_name__			)			)

		def 	lowercase__     (   self					:   Any  ,      __magic_name__					:   int  ,      __magic_name__					:   Dict  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Any  ,      __magic_name__					:   List[str]  ,      __magic_name__					:   Dict=False  ,      __magic_name__					:   Optional[int]=1			)					->    Optional[int]:


					"""simple docstring"""

					self.assertIsInstance(__magic_name__  ,      __magic_name__			)
					self.assertListEqual(
					    [isinstance(__magic_name__  ,      __magic_name__			) for iter_hidden_states in hidden_states]  ,      [True] * len(__magic_name__			)  ,      )
					self.assertEqual(len(__magic_name__			)  ,      (max_length - min_length) * num_beam_groups			)

					for idx, iter_hidden_states in enumerate(__magic_name__			):
								# adds PAD dummy token
								__snake_case					:      str							      =     min_length + idx + 1
								__snake_case					:      Union[str, Any]							      =     (batch_size * num_beam_groups, seq_len, config.hidden_size)
								# check hidden size
								self.assertListEqual(
								    [layer_hidden_states.shape for layer_hidden_states in iter_hidden_states]  ,      [expected_shape] * len(__magic_name__			)  ,      )
					pass

		@slow
		def 	lowercase__     (   self					:   Union[str, Any]			)					->    Union[str, Any]:


					"""simple docstring"""

					for model_name in XLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
								__snake_case					:      Dict							      =     XLMModel.from_pretrained(__magic_name__			)
								self.assertIsNotNone(__magic_name__			)





@require_torch
class  _A (					unittest.TestCase		):

		@slow
		def 	lowercase__     (   self					:   str			)					->    Tuple:


					"""simple docstring"""

					__snake_case					:      Union[str, Any]							      =     XLMWithLMHeadModel.from_pretrained("""xlm-mlm-en-2048"""			)
					model.to(__magic_name__			)
					__snake_case					:      Any							      =     torch.tensor([[14, 4_47]]  ,      dtype=torch.long  ,      device=__magic_name__			)  # the president
					__snake_case					:      Union[str, Any]							      =     [
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					    14,
					    4_47,
					]  # the president the president the president the president the president the president the president the president the president the president
					# TODO(PVP): this and other input_ids I tried for generation give pretty bad results. Not sure why. Model might just not be made for auto-regressive inference
					__snake_case					:      Tuple							      =     model.generate(__magic_name__  ,      do_sample=__magic_name__			)
					self.assertListEqual(output_ids[0].cpu().numpy().tolist()  ,      __magic_name__			)









'''simple docstring'''






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
__UpperCamelCase					    =	logging.get_logger(__name__)







def 					_a						(       _lowerCamelCase ) ->				Any:



			"""simple docstring"""


			__snake_case					:      Union[str, Any]							      =     MobileNetVaConfig(layer_norm_eps=0.0_01 )

			if "_quant" in model_name:
						raise ValueError("""Quantized models are not supported.""" )

			__snake_case					:      List[Any]							      =     re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""    ,	_lowerCamelCase )
			if matches:
						__snake_case					:      Optional[Any]							      =     float(matches[1] )
						__snake_case					:      Union[str, Any]							      =     int(matches[2] )

			# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
			# the usual 1000. The first class (index 0) is "background".
			__snake_case					:      Tuple							      =     1001
			__snake_case					:      Any							      =     """imagenet-1k-id2label.json"""
			__snake_case					:      Optional[Any]							      =     """huggingface/label-files"""
			__snake_case					:      List[Any]							      =     json.load(open(hf_hub_download(_lowerCamelCase    ,	_lowerCamelCase    ,	repo_type="""dataset""" )    ,	"""r""" ) )
			__snake_case					:      Dict							      =     {int(_lowerCamelCase ) + 1: v for k, v in idalabel.items()}
			__snake_case					:      List[str]							      =     """background"""
			__snake_case					:      List[str]							      =     idalabel
			__snake_case					:      List[Any]							      =     {v: k for k, v in idalabel.items()}

			return config







def 					_a						(       ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     """http://images.cocodataset.org/val2017/000000039769.jpg"""
			__snake_case					:      List[Any]							      =     Image.open(requests.get(_lowerCamelCase    ,	stream=_lowerCamelCase ).raw )
			return im







@torch.no_grad()
def 					_a						(       _lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase=False ) ->				Optional[Any]:



			"""simple docstring"""


			__snake_case					:      Optional[int]							      =     get_mobilenet_va_config(_lowerCamelCase )

			# Load 🤗 model
			__snake_case					:      Optional[Any]							      =     MobileNetVaForImageClassification(_lowerCamelCase ).eval()

			# Load weights from TensorFlow checkpoint
			load_tf_weights_in_mobilenet_va(_lowerCamelCase    ,	_lowerCamelCase    ,	_lowerCamelCase )

			# Check outputs on an image, prepared by MobileNetV1ImageProcessor
			__snake_case					:      Optional[int]							      =     MobileNetVaImageProcessor(
			    crop_size={"""width""": config.image_size, """height""": config.image_size}    ,	size={"""shortest_edge""": config.image_size + 32}    ,	)
			__snake_case					:      Tuple							      =     image_processor(images=prepare_img()    ,	return_tensors="""pt""" )
			__snake_case					:      Optional[Any]							      =     model(**_lowerCamelCase )
			__snake_case					:      List[Any]							      =     outputs.logits

			assert logits.shape == (1, 1001)

			if model_name == "mobilenet_v1_1.0_224":
						__snake_case					:      str							      =     torch.tensor([-4.17_39, -1.12_33, 3.12_05] )
			elif model_name == "mobilenet_v1_0.75_192":
						__snake_case					:      Tuple							      =     torch.tensor([-3.94_40, -2.31_41, -0.33_33] )
			else:
						__snake_case					:      List[Any]							      =     None

			if expected_logits is not None:
						assert torch.allclose(logits[0, :3]    ,	_lowerCamelCase    ,	atol=1E-4 )

			Path(_lowerCamelCase ).mkdir(exist_ok=_lowerCamelCase )
			print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
			model.save_pretrained(_lowerCamelCase )
			print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
			image_processor.save_pretrained(_lowerCamelCase )

			if push_to_hub:
						print("""Pushing to the hub...""" )
						__snake_case					:      Optional[Any]							      =     """google/""" + model_name
						image_processor.push_to_hub(_lowerCamelCase )
						model.push_to_hub(_lowerCamelCase )


if __name__ == "__main__":
	__UpperCamelCase					    =	argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    "--model_name",
	    default="mobilenet_v1_1.0_224",
	    type=str,
	    help="Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.",
	)
	parser.add_argument(
	    "--checkpoint_path", required=True, type=str, help="Path to the original TensorFlow checkpoint (.ckpt file)."
	)
	parser.add_argument(
	    "--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
	)
	parser.add_argument(
	    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
	)

	__UpperCamelCase					    =	parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)


