






import os
import sys
import unittest


a__       =     os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, """utils"""))

import get_test_info  # noqa: E402
from get_test_info import (  # noqa: E402
    get_model_to_test_mapping,
    get_model_to_tester_mapping,
    get_test_to_tester_mapping,
)


a__       =     os.path.join("""tests""", """models""", """bert""", """test_modeling_bert.py""")
a__       =     os.path.join("""tests""", """models""", """blip""", """test_modeling_blip.py""")


class    _lowerCAmelCase			(		unittest.TestCase      ):



				"""simple docstring"""



				def 					__magic_name__							(      self    :    List[Any]):






											'''simple docstring'''







											snake_case__	  =       get_test_to_tester_mapping(UpperCamelCase__)
											snake_case__	  =       get_test_to_tester_mapping(UpperCamelCase__)

											snake_case__	  =       {"""BertModelTest""": """BertModelTester"""}

											snake_case__	  =       {
											    """BlipModelTest""": """BlipModelTester""",
											    """BlipTextImageModelTest""": """BlipTextImageModelsModelTester""",
											    """BlipTextModelTest""": """BlipTextModelTester""",
											    """BlipTextRetrievalModelTest""": """BlipTextRetrievalModelTester""",
											    """BlipVQAModelTest""": """BlipVQAModelTester""",
											    """BlipVisionModelTest""": """BlipVisionModelTester""",
											}

											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)
											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)



				def 					__magic_name__							(      self    :    str):






											'''simple docstring'''







											snake_case__	  =       get_model_to_test_mapping(UpperCamelCase__)
											snake_case__	  =       get_model_to_test_mapping(UpperCamelCase__)

											snake_case__	  =       {
											    """BertForMaskedLM""": ["""BertModelTest"""],
											    """BertForMultipleChoice""": ["""BertModelTest"""],
											    """BertForNextSentencePrediction""": ["""BertModelTest"""],
											    """BertForPreTraining""": ["""BertModelTest"""],
											    """BertForQuestionAnswering""": ["""BertModelTest"""],
											    """BertForSequenceClassification""": ["""BertModelTest"""],
											    """BertForTokenClassification""": ["""BertModelTest"""],
											    """BertLMHeadModel""": ["""BertModelTest"""],
											    """BertModel""": ["""BertModelTest"""],
											}

											snake_case__	  =       {
											    """BlipForConditionalGeneration""": ["""BlipTextImageModelTest"""],
											    """BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTest"""],
											    """BlipForQuestionAnswering""": ["""BlipVQAModelTest"""],
											    """BlipModel""": ["""BlipModelTest"""],
											    """BlipTextModel""": ["""BlipTextModelTest"""],
											    """BlipVisionModel""": ["""BlipVisionModelTest"""],
											}

											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)
											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)



				def 					__magic_name__							(      self    :    Dict):






											'''simple docstring'''







											snake_case__	  =       get_model_to_tester_mapping(UpperCamelCase__)
											snake_case__	  =       get_model_to_tester_mapping(UpperCamelCase__)

											snake_case__	  =       {
											    """BertForMaskedLM""": ["""BertModelTester"""],
											    """BertForMultipleChoice""": ["""BertModelTester"""],
											    """BertForNextSentencePrediction""": ["""BertModelTester"""],
											    """BertForPreTraining""": ["""BertModelTester"""],
											    """BertForQuestionAnswering""": ["""BertModelTester"""],
											    """BertForSequenceClassification""": ["""BertModelTester"""],
											    """BertForTokenClassification""": ["""BertModelTester"""],
											    """BertLMHeadModel""": ["""BertModelTester"""],
											    """BertModel""": ["""BertModelTester"""],
											}

											snake_case__	  =       {
											    """BlipForConditionalGeneration""": ["""BlipTextImageModelsModelTester"""],
											    """BlipForImageTextRetrieval""": ["""BlipTextRetrievalModelTester"""],
											    """BlipForQuestionAnswering""": ["""BlipVQAModelTester"""],
											    """BlipModel""": ["""BlipModelTester"""],
											    """BlipTextModel""": ["""BlipTextModelTester"""],
											    """BlipVisionModel""": ["""BlipVisionModelTester"""],
											}

											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)
											self.assertEqual(get_test_info.to_json(UpperCamelCase__)			,		UpperCamelCase__)








from typing import List, Optional, Union

import numpy as np
import tensorflow as tf

from .utils import logging


a__       =     logging.get_logger(__name__)

def _UpperCAmelCase   (	a						:	Union[tf.Tensor, np.ndarray] ):
							if isinstance(a  ,					np.ndarray ):
														return list(tensor.shape )

							snake_case__	  =       tf.shape(a )

							if tensor.shape == tf.TensorShape(a ):
														return dynamic

							snake_case__	  =       tensor.shape.as_list()

							return [dynamic[i] if s is None else s for i, s in enumerate(a )]

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	Optional[int] = None  ,					a						:	Optional[str] = None ):
							return tf.nn.softmax(logits=logits + 1e-9  ,					axis=a  ,					name=a )

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Union[str, Any]  ,					a						:	Dict  ,					a						:	int=1e-5  ,					a						:	Dict=-1 ):
							# This is a very simplified functional layernorm, designed to duplicate
							# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
							# models in Transformers.

							if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(a  ,					a ):
														raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )

							# Get mean and variance on the axis to be normalized
							snake_case__  , snake_case__	  =       tf.nn.moments(a  ,					axes=[axis]  ,					keepdims=a )

							if axis != -1:
														# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
														# on every dimension except axis
														snake_case__	  =       [1] * inputs.shape.rank
														snake_case__	  =       shape_list(a )[axis]
														snake_case__	  =       tf.reshape(a  ,					a )
														snake_case__	  =       tf.reshape(a  ,					a )

							# Compute layer normalization using the batch_normalization
							# function.
							snake_case__	  =       tf.nn.batch_normalization(
							    a  ,					a  ,					a  ,					offset=a  ,					scale=a  ,					variance_epsilon=a  ,					)
							return outputs

def _UpperCAmelCase   (	a						:	Optional[int]  ,					a						:	Dict=0  ,					a						:	List[Any]=-1 ):
							# Replicates the behavior of torch.flatten in TF

							# If end_dim or start_dim is negative, count them from the end
							if end_dim < 0:
														end_dim += input.shape.rank
							if start_dim < 0:
														start_dim += input.shape.rank

							if start_dim == end_dim:
														return input

							snake_case__	  =       tf.shape(a )
							snake_case__	  =       tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
							snake_case__	  =       tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]]  ,					axis=0 )
							return tf.reshape(a  ,					a )

def _UpperCAmelCase   (	a						:	tf.Tensor ):
							if not isinstance(a  ,					tf.Tensor ):
														snake_case__	  =       tf.convert_to_tensor(a )  # Catches stray NumPy inputs
							if encoder_attention_mask.shape.rank == 3:
														snake_case__	  =       encoder_attention_mask[:, None, :, :]
							if encoder_attention_mask.shape.rank == 2:
														snake_case__	  =       encoder_attention_mask[:, None, None, :]
							# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
							# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
							# /transformer/transformer_layers.py#L270
							# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
							# encoder_extended_attention_mask.transpose(-1, -2))
							snake_case__	  =       (
							    tf.cast(1  ,					encoder_attention_mask.dtype ) - encoder_extended_attention_mask
							) * encoder_extended_attention_mask.dtype.min

							return encoder_extended_attention_mask

def _UpperCAmelCase   (	a						:	tf.Tensor  ,					a						:	int  ,					a						:	str = "input_ids" ):
							tf.debugging.assert_less(
							    a  ,					tf.cast(a  ,					dtype=tensor.dtype )  ,					message=(
							        F'''The maximum value of {tensor_name} ({tf.math.reduce_max(a )}) must be smaller than the embedding '''
							        F'''layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'''
							    )  ,					)

def _UpperCAmelCase   (	a						:	str  ,					a						:	Tuple  ,					a						:	Optional[int] ):
							snake_case__	  =       6_4512
							# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
							# because in that case even chunking the array would not make the saving
							# possible.
							snake_case__	  =       [x for x in data if len(a ) > HDF5_OBJECT_HEADER_LIMIT]

							# Expecting this to never be true.
							if bad_attributes:
														raise RuntimeError(
														    """The following attributes cannot be saved to HDF5 file because """
														    F'''they are larger than {HDF5_OBJECT_HEADER_LIMIT} '''
														    F'''bytes: {bad_attributes}''' )

							snake_case__	  =       np.asarray(a )

							snake_case__	  =       1
							snake_case__	  =       np.array_split(a  ,					a )

							# This will never loop forever thanks to the test above.
							while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
														num_chunks += 1
														snake_case__	  =       np.array_split(a  ,					a )

							if num_chunks > 1:
														for chunk_id, chunk_data in enumerate(a ):
																					snake_case__	  =       chunk_data
							else:
														snake_case__	  =       data

def _UpperCAmelCase   (	a						:	List[Any]  ,					a						:	Optional[int] ):
							if name in group.attrs:
														snake_case__	  =       [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs[name]]
							else:
														snake_case__	  =       []
														snake_case__	  =       0
														while "%s%d" % (name, chunk_id) in group.attrs:
																					data.extend(
																					    [n.decode("""utf8""" ) if hasattr(a  ,					"""decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
																					chunk_id += 1
							return data



def _UpperCAmelCase   (	a						:	Optional[int] ):

							def _expand_single_ad_tensor(a						:	Any ):
														if isinstance(a  ,					tf.Tensor ) and t.shape.rank == 1:
																					return tf.expand_dims(a  ,					axis=-1 )
														return t

							return tf.nest.map_structure(_expand_single_ad_tensor  ,					a )
