
"""simple docstring"""






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow

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


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import (
       TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
       FlaubertConfig,
       TFFlaubertForMultipleChoice,
       TFFlaubertForQuestionAnsweringSimple,
       TFFlaubertForSequenceClassification,
       TFFlaubertForTokenClassification,
       TFFlaubertModel,
       TFFlaubertWithLMHeadModel,
   )


class     __lowerCamelCase  :

     '''simple docstring'''


     def __init__(      self    ,				__UpperCAmelCase    ,				)       ->							str:
          _a    	=				parent
          _a    	=				13
          _a    	=				7
          _a    	=				True
          _a    	=				True
          _a    	=				True
          _a    	=				True
          _a    	=				True
          _a    	=				False
          _a    	=				False
          _a    	=				False
          _a    	=				2
          _a    	=				99
          _a    	=				0
          _a    	=				32
          _a    	=				2
          _a    	=				4
          _a    	=				0.1
          _a    	=				0.1
          _a    	=				512
          _a    	=				16
          _a    	=				2
          _a    	=				0.02
          _a    	=				3
          _a    	=				4
          _a    	=				'''last'''
          _a    	=				True
          _a    	=				None
          _a    	=				0


     def 							_UpperCAmelCase      (      self							)       ->							List[Any]:
          _a    	=				ids_tensor([self.batch_size, self.seq_length]    ,				self.vocab_size							)
          _a    	=				random_attention_mask([self.batch_size, self.seq_length]    ,				dtype=tf.floataa							)

          _a    	=				None
          if self.use_input_lengths:
               _a    	=				(
                   ids_tensor([self.batch_size]    ,				vocab_size=2							) + self.seq_length - 2
               )  # small variation of seq_length

          _a    	=				None
          if self.use_token_type_ids:
               _a    	=				ids_tensor([self.batch_size, self.seq_length]    ,				self.n_langs							)

          _a    	=				None
          _a    	=				None
          _a    	=				None
          if self.use_labels:
               _a    	=				ids_tensor([self.batch_size]    ,				self.type_sequence_label_size							)
               _a    	=				ids_tensor([self.batch_size, self.seq_length]    ,				self.num_labels							)
               _a    	=				ids_tensor([self.batch_size]    ,				2    ,				dtype=tf.floataa							)
               _a    	=				ids_tensor([self.batch_size]    ,				self.num_choices							)

          _a    	=				FlaubertConfig(
              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    ,				bos_token_id=self.bos_token_id    ,				)

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


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Optional[int]:
          _a    	=				TFFlaubertModel(config=__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          _a    	=				model(__UpperCAmelCase							)

          _a    	=				[input_ids, input_mask]
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.last_hidden_state.shape    ,				(self.batch_size, self.seq_length, self.hidden_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							int:
          _a    	=				TFFlaubertWithLMHeadModel(__UpperCAmelCase							)

          _a    	=				{'''input_ids''': input_ids, '''lengths''': input_lengths, '''langs''': token_type_ids}
          _a    	=				model(__UpperCAmelCase							)

          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.vocab_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							List[str]:
          _a    	=				TFFlaubertForQuestionAnsweringSimple(__UpperCAmelCase							)

          _a    	=				{'''input_ids''': input_ids, '''lengths''': input_lengths}

          _a    	=				model(__UpperCAmelCase							)

          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 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Dict:
          _a    	=				TFFlaubertForSequenceClassification(__UpperCAmelCase							)

          _a    	=				{'''input_ids''': input_ids, '''lengths''': input_lengths}

          _a    	=				model(__UpperCAmelCase							)

          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.type_sequence_label_size)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							Optional[Any]:
          _a    	=				self.num_labels
          _a    	=				TFFlaubertForTokenClassification(config=__UpperCAmelCase							)
          _a    	=				{'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.seq_length, self.num_labels)							)


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				)       ->							List[str]:
          _a    	=				self.num_choices
          _a    	=				TFFlaubertForMultipleChoice(config=__UpperCAmelCase							)
          _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
          _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
          _a    	=				tf.tile(tf.expand_dims(__UpperCAmelCase    ,				1							)    ,				(1, self.num_choices, 1)							)
          _a    	=				{
              '''input_ids''': multiple_choice_inputs_ids,
              '''attention_mask''': multiple_choice_input_mask,
              '''token_type_ids''': multiple_choice_token_type_ids,
          }
          _a    	=				model(__UpperCAmelCase							)
          self.parent.assertEqual(result.logits.shape    ,				(self.batch_size, self.num_choices)							)





     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.prepare_config_and_inputs()
          (
              (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      (
              _a					
          )	,      					
          )    	=				config_and_inputs
          _a    	=				{
              '''input_ids''': input_ids,
              '''token_type_ids''': token_type_ids,
              '''langs''': token_type_ids,
              '''lengths''': input_lengths,
          }
          return config, inputs_dict







@require_tf
class     __lowerCamelCase  (					a__  ,					a__  ,					unittest.TestCase				):

     '''simple docstring'''
     A_       :	List[Any]    =		(
         (
             TFFlaubertModel,
             TFFlaubertWithLMHeadModel,
             TFFlaubertForSequenceClassification,
             TFFlaubertForQuestionAnsweringSimple,
             TFFlaubertForTokenClassification,
             TFFlaubertForMultipleChoice,
         )
         if is_tf_available()
         else ()
     )
     A_       :	List[str]    =		(
         (TFFlaubertWithLMHeadModel,) if is_tf_available() else ()
     )  # TODO (PVP): Check other models whether language generation is also applicable
     A_       :	List[str]    =		(
         {
             'feature-extraction': TFFlaubertModel,
             'fill-mask': TFFlaubertWithLMHeadModel,
             'question-answering': TFFlaubertForQuestionAnsweringSimple,
             'text-classification': TFFlaubertForSequenceClassification,
             'token-classification': TFFlaubertForTokenClassification,
             'zero-shot': TFFlaubertForSequenceClassification,
         }
         if is_tf_available()
         else {}
     )
     A_       :	Optional[Any]    =		False
     A_       :	Union[str, Any]    =		False


     def 							_UpperCAmelCase      (      self    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase							)       ->							Optional[Any]:







          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 							_UpperCAmelCase      (      self							)       ->							int:
          _a    	=				TFFlaubertModelTester(self							)
          _a    	=				ConfigTester(self    ,				config_class=__UpperCAmelCase    ,				emb_dim=37							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          self.config_tester.run_common_tests()


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_model(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_lm_head(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Dict:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_qa(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Any:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_sequence_classif(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Optional[int]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_for_token_classification(*__UpperCAmelCase							)


     def 							_UpperCAmelCase      (      self							)       ->							Union[str, Any]:
          _a    	=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_flaubert_for_multiple_choice(*__UpperCAmelCase							)





     @slow
     def 							_UpperCAmelCase      (      self							)       ->							Optional[Any]:







          for model_name in TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               _a    	=				TFFlaubertModel.from_pretrained(__UpperCAmelCase							)
               self.assertIsNotNone(__UpperCAmelCase							)







@require_tf
@require_sentencepiece
@require_tokenizers
class     __lowerCamelCase  (					unittest.TestCase				):

     '''simple docstring'''


     @slow
     def 							_UpperCAmelCase      (      self							)       ->							List[str]:
          _a    	=				TFFlaubertModel.from_pretrained('''jplu/tf-flaubert-small-cased'''							)

          _a    	=				tf.convert_to_tensor(
              [[0, 158, 735, 2592, 1424, 6727, 82, 1]]    ,				dtype=tf.intaa    ,				)  # "J'aime flaubert !"

          _a    	=				model(__UpperCAmelCase							)[0]
          _a    	=				tf.TensorShape((1, 8, 512)							)
          self.assertEqual(output.shape    ,				__UpperCAmelCase							)
          # compare the actual values for a slice.
          _a    	=				tf.convert_to_tensor(
              [
                  [
                      [-1.8768773, -1.566555, 0.27072418],
                      [-1.6920038, -0.5873505, 1.9329599],
                      [-2.9563985, -1.6993835, 1.7972052],
                  ]
              ]    ,				dtype=tf.floataa    ,				)

          self.assertTrue(np.allclose(output[:, :3, :3].numpy()    ,				expected_slice.numpy()    ,				atol=1e-4							)							)



"""simple docstring"""






import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def       A_   (   _lowerCAmelCase     :					Dict       ):






     """simple docstring"""

     if "img_encoder.pos_embed" in name:
          _a    	=				name.replace('''img_encoder.pos_embed''',     '''vision_model.embeddings.position_embeddings'''       )
     if "img_encoder.patch_embed.proj" in name:
          _a    	=				name.replace('''img_encoder.patch_embed.proj''',     '''vision_model.embeddings.patch_embeddings.projection'''       )
     if "img_encoder.patch_embed.norm" in name:
          _a    	=				name.replace('''img_encoder.patch_embed.norm''',     '''vision_model.embeddings.layernorm'''       )
     if "img_encoder.layers" in name:
          _a    	=				name.replace('''img_encoder.layers''',     '''vision_model.encoder.stages'''       )
     if "blocks" in name and "res" not in name:
          _a    	=				name.replace('''blocks''',     '''layers'''       )
     if "attn" in name and "pre_assign" not in name:
          _a    	=				name.replace('''attn''',     '''self_attn'''       )
     if "proj" in name and "self_attn" in name and "text" not in name:
          _a    	=				name.replace('''proj''',     '''out_proj'''       )
     if "pre_assign_attn.attn.proj" in name:
          _a    	=				name.replace('''pre_assign_attn.attn.proj''',     '''pre_assign_attn.attn.out_proj'''       )
     if "norm1" in name:
          _a    	=				name.replace('''norm1''',     '''layer_norm1'''       )
     if "norm2" in name and "pre_assign" not in name:
          _a    	=				name.replace('''norm2''',     '''layer_norm2'''       )
     if "img_encoder.norm" in name:
          _a    	=				name.replace('''img_encoder.norm''',     '''vision_model.layernorm'''       )
     # text encoder
     if "text_encoder.token_embedding" in name:
          _a    	=				name.replace('''text_encoder.token_embedding''',     '''text_model.embeddings.token_embedding'''       )
     if "text_encoder.positional_embedding" in name:
          _a    	=				name.replace('''text_encoder.positional_embedding''',     '''text_model.embeddings.position_embedding.weight'''       )
     if "text_encoder.transformer.resblocks." in name:
          _a    	=				name.replace('''text_encoder.transformer.resblocks.''',     '''text_model.encoder.layers.'''       )
     if "ln_1" in name:
          _a    	=				name.replace('''ln_1''',     '''layer_norm1'''       )
     if "ln_2" in name:
          _a    	=				name.replace('''ln_2''',     '''layer_norm2'''       )
     if "c_fc" in name:
          _a    	=				name.replace('''c_fc''',     '''fc1'''       )
     if "c_proj" in name:
          _a    	=				name.replace('''c_proj''',     '''fc2'''       )
     if "text_encoder" in name:
          _a    	=				name.replace('''text_encoder''',     '''text_model'''       )
     if "ln_final" in name:
          _a    	=				name.replace('''ln_final''',     '''final_layer_norm'''       )
     # projection layers
     if "img_projector.linear_hidden." in name:
          _a    	=				name.replace('''img_projector.linear_hidden.''',     '''visual_projection.'''       )
     if "img_projector.linear_out." in name:
          _a    	=				name.replace('''img_projector.linear_out.''',     '''visual_projection.3.'''       )
     if "text_projector.linear_hidden" in name:
          _a    	=				name.replace('''text_projector.linear_hidden''',     '''text_projection'''       )
     if "text_projector.linear_out" in name:
          _a    	=				name.replace('''text_projector.linear_out''',     '''text_projection.3'''       )

     return name
def       A_   (   _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					int       ):






     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          _a    	=				orig_state_dict.pop(_lowerCAmelCase       )

          if "qkv" in key:
               # weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a    	=				key.split('''.'''       )
               _a	,      _a    	=				int(key_split[2]       ), int(key_split[4]       )
               _a    	=				config.vision_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[dim : dim * 2, :]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          elif "in_proj" in key:
               # weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
               # we need to split them up into separate matrices/vectors
               _a    	=				key.split('''.'''       )
               _a    	=				int(key_split[3]       )
               _a    	=				config.text_config.hidden_size
               if "weight" in key:
                    _a    	=				val[:dim, :]
                    _a    	=				val[
                        dim : dim * 2, :
                    ]
                    _a    	=				val[-dim:, :]
               else:
                    _a    	=				val[:dim]
                    _a    	=				val[dim : dim * 2]
                    _a    	=				val[-dim:]
          else:
               _a    	=				rename_key(_lowerCAmelCase       )
               # squeeze if necessary
               if (
                   "text_projection.0" in new_name
                   or "text_projection.3" in new_name
                   or "visual_projection.0" in new_name
                   or "visual_projection.3" in new_name
               ):
                    _a    	=				val.squeeze_()
               else:
                    _a    	=				val

     return orig_state_dict
def       A_   (   ):






     """simple docstring"""

     _a    	=				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     _a    	=				Image.open(requests.get(_lowerCAmelCase,     stream=_lowerCAmelCase       ).raw       )
     return im
@torch.no_grad()
def       A_   (   _lowerCAmelCase     :					Dict,     _lowerCAmelCase     :					List[str],     _lowerCAmelCase     :					Dict="groupvit-gcc-yfcc",     _lowerCAmelCase     :					str=False       ):






     """simple docstring"""

     _a    	=				GroupViTConfig()
     _a    	=				GroupViTModel(_lowerCAmelCase       ).eval()

     _a    	=				torch.load(_lowerCAmelCase,     map_location='''cpu'''       )['''model''']
     _a    	=				convert_state_dict(_lowerCAmelCase,     _lowerCAmelCase       )
     _a	,      _a    	=				model.load_state_dict(_lowerCAmelCase,     strict=_lowerCAmelCase       )
     assert missing_keys == ["text_model.embeddings.position_ids"]
     assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(_lowerCAmelCase       ) == 0)

     # verify result
     _a    	=				CLIPProcessor.from_pretrained('''openai/clip-vit-base-patch32'''       )
     _a    	=				prepare_img()
     _a    	=				processor(text=['''a photo of a cat''', '''a photo of a dog'''],     images=_lowerCAmelCase,     padding=_lowerCAmelCase,     return_tensors='''pt'''       )

     with torch.no_grad():
          _a    	=				model(**_lowerCAmelCase       )

     if model_name == "groupvit-gcc-yfcc":
          _a    	=				torch.tensor([[1_3.3_5_2_3, 6.3_6_2_9]]       )
     elif model_name == "groupvit-gcc-redcaps":
          _a    	=				torch.tensor([[1_6.1_8_7_3, 8.6_2_3_0]]       )
     else:
          raise ValueError(f'Model name {model_name} not supported.'       )
     assert torch.allclose(outputs.logits_per_image,     _lowerCAmelCase,     atol=1e-3       )

     processor.save_pretrained(_lowerCAmelCase       )
     model.save_pretrained(_lowerCAmelCase       )
     print('''Successfully saved processor and model to''',     _lowerCAmelCase       )

     if push_to_hub:
          print('''Pushing to the hub...'''       )
          processor.push_to_hub(_lowerCAmelCase,     organization='''nielsr'''       )
          model.push_to_hub(_lowerCAmelCase,     organization='''nielsr'''       )


if __name__ == "__main__":
   __snake_case		=       argparse.ArgumentParser()
   parser.add_argument(
       '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to dump the processor and PyTorch model.'''
   )
   parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to GroupViT checkpoint''')
   parser.add_argument(
       '''--model_name''',
       default='''groupvit-gccy-fcc''',
       type=str,
       help='''Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
       help='''Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.''',
   )
   __snake_case		=       parser.parse_args()

   convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)