





import tempfile
import unittest

import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError

from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax


if is_flax_available():
     import os

     from flax.core.frozen_dict import unfreeze
     from flax.traverse_util import flatten_dict

     from transformers import FlaxBertModel

     __A							   =							"0.12"  # assumed parallelism: 8



@require_flax
@is_staging_test
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 @classmethod
 def 				SCREAMING_SNAKE_CASE_   (cls							:  str)			->Optional[Any]:



   '''simple docstring'''



   lowerCamelCase__: Optional[int]         =TOKEN
   HfFolder.save_token(UpperCAmelCase_)

 @classmethod
 def 				SCREAMING_SNAKE_CASE_   (cls							:  Optional[Any])			->int:



   '''simple docstring'''



   try:
     delete_repo(token=cls._token			,					repo_id="test-model-flax")
   except HTTPError:
     pass

   try:
     delete_repo(token=cls._token			,					repo_id="valid_org/test-model-flax-org")
   except HTTPError:
     pass

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->Tuple:



   '''simple docstring'''



   lowerCamelCase__: str         =BertConfig(
       vocab_size=99			,					hidden_size=32			,					num_hidden_layers=5			,					num_attention_heads=4			,					intermediate_size=37)
   lowerCamelCase__: Dict         =FlaxBertModel(UpperCAmelCase_)
   model.push_to_hub("test-model-flax"			,					use_auth_token=self._token)

   lowerCamelCase__: Union[str, Any]         =FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""")

   lowerCamelCase__: str         =flatten_dict(unfreeze(model.params))
   lowerCamelCase__: Union[str, Any]         =flatten_dict(unfreeze(new_model.params))

   for key in base_params.keys():
     lowerCamelCase__: List[str]         =(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(UpperCAmelCase_			,					1E-3			,					msg=F"""{key} not identical""")

   # Reset repo
   delete_repo(token=self._token			,					repo_id="test-model-flax")

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(UpperCAmelCase_			,					repo_id="test-model-flax"			,					push_to_hub=UpperCAmelCase_			,					use_auth_token=self._token)

   lowerCamelCase__: Tuple         =FlaxBertModel.from_pretrained(F"""{USER}/test-model-flax""")

   lowerCamelCase__: Optional[int]         =flatten_dict(unfreeze(model.params))
   lowerCamelCase__: List[str]         =flatten_dict(unfreeze(new_model.params))

   for key in base_params.keys():
     lowerCamelCase__: List[Any]         =(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(UpperCAmelCase_			,					1E-3			,					msg=F"""{key} not identical""")







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



   '''simple docstring'''



   lowerCamelCase__: Any         =BertConfig(
       vocab_size=99			,					hidden_size=32			,					num_hidden_layers=5			,					num_attention_heads=4			,					intermediate_size=37)
   lowerCamelCase__: List[str]         =FlaxBertModel(UpperCAmelCase_)
   model.push_to_hub("valid_org/test-model-flax-org"			,					use_auth_token=self._token)

   lowerCamelCase__: Optional[int]         =FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")

   lowerCamelCase__: int         =flatten_dict(unfreeze(model.params))
   lowerCamelCase__: Any         =flatten_dict(unfreeze(new_model.params))

   for key in base_params.keys():
     lowerCamelCase__: List[str]         =(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(UpperCAmelCase_			,					1E-3			,					msg=F"""{key} not identical""")

   # Reset repo
   delete_repo(token=self._token			,					repo_id="valid_org/test-model-flax-org")

   # Push to hub via save_pretrained
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(
         UpperCAmelCase_			,					repo_id="valid_org/test-model-flax-org"			,					push_to_hub=UpperCAmelCase_			,					use_auth_token=self._token)

   lowerCamelCase__: int         =FlaxBertModel.from_pretrained("valid_org/test-model-flax-org")

   lowerCamelCase__: Union[str, Any]         =flatten_dict(unfreeze(model.params))
   lowerCamelCase__: Union[str, Any]         =flatten_dict(unfreeze(new_model.params))

   for key in base_params.keys():
     lowerCamelCase__: Tuple         =(base_params[key] - new_params[key]).sum().item()
     self.assertLessEqual(UpperCAmelCase_			,					1E-3			,					msg=F"""{key} not identical""")





def 	lowerCAmelCase_					(			__a      ,       __a						)	->						Union[str, Any]:







  """simple docstring"""





  lowerCamelCase__: int         =True
  lowerCamelCase__: Any         =flatten_dict(modela.params						)
  lowerCamelCase__: Any         =flatten_dict(modela.params						)
  for key in flat_params_a.keys():
    if np.sum(np.abs(flat_params_a[key] - flat_params_a[key]						)						) > 1e-4:
      lowerCamelCase__: Dict         =False

  return models_are_equal






@require_flax
class   _SCREAMING_SNAKE_CASE		(      unittest.TestCase       ):







 '''simple docstring'''

 def 				SCREAMING_SNAKE_CASE_   (self							:  str)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Optional[Any]         =BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
   lowerCamelCase__: str         =FlaxBertModel(UpperCAmelCase_)

   lowerCamelCase__: int         ="bert"
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(UpperCAmelCase_			,					UpperCAmelCase_))

     with self.assertRaises(UpperCAmelCase_):
       lowerCamelCase__: Tuple         =FlaxBertModel.from_pretrained(UpperCAmelCase_)

     lowerCamelCase__: Optional[int]         =FlaxBertModel.from_pretrained(UpperCAmelCase_			,					subfolder=UpperCAmelCase_)

   self.assertTrue(check_models_equal(UpperCAmelCase_			,					UpperCAmelCase_))

 def 				SCREAMING_SNAKE_CASE_   (self							:  Any)			->Any:



   '''simple docstring'''



   lowerCamelCase__: Union[str, Any]         =BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only")
   lowerCamelCase__: List[Any]         =FlaxBertModel(UpperCAmelCase_)

   lowerCamelCase__: int         ="bert"
   with tempfile.TemporaryDirectory() as tmp_dir:
     model.save_pretrained(os.path.join(UpperCAmelCase_			,					UpperCAmelCase_)			,					max_shard_size="10KB")

     with self.assertRaises(UpperCAmelCase_):
       lowerCamelCase__: Dict         =FlaxBertModel.from_pretrained(UpperCAmelCase_)

     lowerCamelCase__: str         =FlaxBertModel.from_pretrained(UpperCAmelCase_			,					subfolder=UpperCAmelCase_)

   self.assertTrue(check_models_equal(UpperCAmelCase_			,					UpperCAmelCase_))

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



   '''simple docstring'''



   lowerCamelCase__: str         ="bert"
   lowerCamelCase__: Optional[Any]         ="hf-internal-testing/tiny-random-bert-subfolder"

   with self.assertRaises(UpperCAmelCase_):
     lowerCamelCase__: Union[str, Any]         =FlaxBertModel.from_pretrained(UpperCAmelCase_)

   lowerCamelCase__: Tuple         =FlaxBertModel.from_pretrained(UpperCAmelCase_			,					subfolder=UpperCAmelCase_)

   self.assertIsNotNone(UpperCAmelCase_)







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



   '''simple docstring'''



   lowerCamelCase__: List[str]         ="bert"
   lowerCamelCase__: Optional[int]         ="hf-internal-testing/tiny-random-bert-sharded-subfolder"
   with self.assertRaises(UpperCAmelCase_):
     lowerCamelCase__: Dict         =FlaxBertModel.from_pretrained(UpperCAmelCase_)

   lowerCamelCase__: Any         =FlaxBertModel.from_pretrained(UpperCAmelCase_			,					subfolder=UpperCAmelCase_)

   self.assertIsNotNone(UpperCAmelCase_)



'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...feature_extraction_utils import FeatureExtractionMixin
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import TensorType, logging


lowerCAmelCase					:    Any           = logging.get_logger(__name__)

lowerCAmelCase					:    Optional[int]           = {
    'deepmind/language-perceiver': 'https://huggingface.co/deepmind/language-perceiver/resolve/main/config.json',
    # See all Perceiver models at https://huggingface.co/models?filter=perceiver
}


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):
				lowerCAmelCase_     			=							"""perceiver"""







				def __init__(       self						,				A_=256						,				A_=1280						,				A_=768						,				A_=1						,				A_=26						,				A_=8						,				A_=8						,				A_=None						,				A_=None						,				A_="kv"						,				A_=1						,				A_=1						,				A_="gelu"						,				A_=0.1						,				A_=0.02						,				A_=1e-12						,				A_=True						,				A_=262						,				A_=2048						,				A_=56						,				A_=[368, 496]						,				A_=16						,				A_=1920						,				A_=16						,				A_=[1, 16, 224, 224]						,				**A_						,				)->		str:







											'''simple docstring'''






											super().__init__(**A_     )

											UpperCamelCase   			=     num_latents
											UpperCamelCase   			=     d_latents
											UpperCamelCase   			=     d_model
											UpperCamelCase   			=     num_blocks
											UpperCamelCase   			=     num_self_attends_per_block
											UpperCamelCase   			=     num_self_attention_heads
											UpperCamelCase   			=     num_cross_attention_heads
											UpperCamelCase   			=     qk_channels
											UpperCamelCase   			=     v_channels
											UpperCamelCase   			=     cross_attention_shape_for_attention
											UpperCamelCase   			=     self_attention_widening_factor
											UpperCamelCase   			=     cross_attention_widening_factor
											UpperCamelCase   			=     hidden_act
											UpperCamelCase   			=     attention_probs_dropout_prob
											UpperCamelCase   			=     initializer_range
											UpperCamelCase   			=     layer_norm_eps
											UpperCamelCase   			=     use_query_residual
											# masked language modeling attributes
											UpperCamelCase   			=     vocab_size
											UpperCamelCase   			=     max_position_embeddings
											# image classification attributes
											UpperCamelCase   			=     image_size
											# flow attributes
											UpperCamelCase   			=     train_size
											# multimodal autoencoding attributes
											UpperCamelCase   			=     num_frames
											UpperCamelCase   			=     audio_samples_per_frame
											UpperCamelCase   			=     samples_per_patch
											UpperCamelCase   			=     output_shape


class 			SCREAMING_SNAKE_CASE__      (     snake_case_):







				@property
				def 						UpperCAmelCase_    (       self     )->		Mapping[str, Mapping[int, str]]:







											'''simple docstring'''






											if self.task == "multiple-choice":
																		UpperCamelCase   			=     {0: 'batch', 1: 'choice', 2: 'sequence'}
											else:
																		UpperCamelCase   			=     {0: 'batch', 1: 'sequence'}
											return OrderedDict(
											    [
											        ('inputs', dynamic_axis),
											        ('attention_mask', dynamic_axis),
											    ]     )







				@property
				def 						UpperCAmelCase_    (       self     )->		float:







											'''simple docstring'''






											return 1e-4







				def 						UpperCAmelCase_    (       self						,				A_						,				A_ = -1						,				A_ = -1						,				A_ = -1						,				A_ = False						,				A_ = None						,				A_ = 3						,				A_ = 40						,				A_ = 40						,				)->		Mapping[str, Any]:







											'''simple docstring'''






											if isinstance(A_						,				A_     ):
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_batch						,				num_token_to_add=0     )
																		# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
																		UpperCamelCase   			=     preprocessor.num_special_tokens_to_add(A_     )
																		UpperCamelCase   			=     compute_effective_axis_dimension(
																		    A_						,				fixed_dimension=OnnxConfig.default_fixed_sequence						,				num_token_to_add=A_     )
																		# Generate dummy inputs according to compute batch and sequence
																		UpperCamelCase   			=     [' '.join(['a']     ) * seq_length] * batch_size
																		UpperCamelCase   			=     dict(preprocessor(A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('input_ids'     )
																		return inputs
											elif isinstance(A_						,				A_     ) and preprocessor.model_input_names[0] == "pixel_values":
																		# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
																		UpperCamelCase   			=     compute_effective_axis_dimension(A_						,				fixed_dimension=OnnxConfig.default_fixed_batch     )
																		UpperCamelCase   			=     self._generate_dummy_images(A_						,				A_						,				A_						,				A_     )
																		UpperCamelCase   			=     dict(preprocessor(images=A_						,				return_tensors=A_     )     )
																		UpperCamelCase   			=     inputs.pop('pixel_values'     )
																		return inputs
											else:
																		raise ValueError(
																		    'Unable to generate dummy inputs for the model. Please provide a tokenizer or a preprocessor.'     )




