
'''simple docstring'''

import csv
import os

import pytest

from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.csv import CsvDatasetReader, CsvDatasetWriter

from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Any      )					->       Optional[Any]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			assert dataset.num_rows == 4
			assert dataset.num_columns == 3
			assert dataset.column_names == ["col_1", "col_2", "col_3"]
			for feature, expected_dtype in expected_features.items():
						assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,	[False, True]      )
def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Optional[Any]						,	UpperCamelCase_					:     Optional[Any]      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_						,	keep_in_memory=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize(
    "features"						,	[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,	)
def   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     Tuple      )					->       Union[str, Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	features=UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     str      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_						,	split=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )
			assert dataset.split == split if split else "train"







@pytest.mark.parametrize("path_type"						,	[str, list]      )
def   A     (      UpperCamelCase_					:     Any						,	UpperCamelCase_					:     Tuple						,	UpperCamelCase_					:     List[str]      )					->       Optional[int]:







			'''simple docstring'''



			if issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							csv_path
			elif issubclass(UpperCamelCase_						,	UpperCamelCase_      ):
						lowerCAmelCase__								=							[csv_path]
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_dataset(UpperCamelCase_						,	UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     List[str]=("train",)      )					->       Optional[int]:







			'''simple docstring'''



			assert isinstance(UpperCamelCase_						,	UpperCamelCase_      )
			for split in splits:
						lowerCAmelCase__								=							dataset_dict[split]
						assert dataset.num_rows == 4
						assert dataset.num_columns == 3
						assert dataset.column_names == ["col_1", "col_2", "col_3"]
						for feature, expected_dtype in expected_features.items():
									assert dataset.features[feature].dtype == expected_dtype







@pytest.mark.parametrize("keep_in_memory"						,	[False, True]      )
def   A     (      UpperCamelCase_					:     Optional[int]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Optional[int]      )					->       Optional[Any]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
						lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_						,	keep_in_memory=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize(
    "features"						,	[
        None,
        {"col_1": "string", "col_2": "int64", "col_3": "float64"},
        {"col_1": "string", "col_2": "string", "col_3": "string"},
        {"col_1": "int32", "col_2": "int32", "col_3": "int32"},
        {"col_1": "float32", "col_2": "float32", "col_3": "float32"},
    ]						,	)
def   A     (      UpperCamelCase_					:     str						,	UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     Dict      )					->       Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			# CSV file loses col_1 string dtype information: default now is "int64" instead of "string"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							features.copy() if features else default_expected_features
			lowerCAmelCase__								=							(
			    Features({feature: Value(UpperCamelCase_      ) for feature, dtype in features.items()}      ) if features is not None else None
			)
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	features=UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_      )







@pytest.mark.parametrize("split"						,	[None, NamedSplit("train"      ), "train", "test"]      )
def   A     (      UpperCamelCase_					:     List[Any]						,	UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     List[Any]      )					->       Optional[int]:







			'''simple docstring'''



			if split:
						lowerCAmelCase__								=							{split: csv_path}
			else:
						lowerCAmelCase__								=							{"train": csv_path, "test": csv_path}
			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							{"col_1": "int64", "col_2": "int64", "col_3": "float64"}
			lowerCAmelCase__								=							CsvDatasetReader(UpperCamelCase_						,	cache_dir=UpperCamelCase_      ).read()
			_check_csv_datasetdict(UpperCamelCase_						,	UpperCamelCase_						,	splits=list(path.keys()      )      )
			assert all(dataset[split].split == split for split in path.keys()      )







def   A     (      UpperCamelCase_					:     str      )					->       List[str]:







			'''simple docstring'''



			with open(UpperCamelCase_						,	encoding="utf-8"      ) as csvfile:
						yield from csv.reader(UpperCamelCase_      )







def   A     (      UpperCamelCase_					:     Dict						,	UpperCamelCase_					:     int      )					->       int:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=1      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

			for rowa, rowa in zip(UpperCamelCase_						,	UpperCamelCase_      ):
						assert rowa == rowa







def   A     (      UpperCamelCase_					:     Union[str, Any]						,	UpperCamelCase_					:     Dict      )					->       List[str]:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=2      ).write()

			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )
			lowerCAmelCase__								=							iter_csv_file(UpperCamelCase_      )

			for rowa, rowa in zip(UpperCamelCase_						,	UpperCamelCase_      ):
						assert rowa == rowa







def   A     (      UpperCamelCase_					:     List[str]						,	UpperCamelCase_					:     Tuple      )					->       Any:







			'''simple docstring'''



			lowerCAmelCase__								=							tmp_path / "cache"
			lowerCAmelCase__								=							os.path.join(UpperCamelCase_						,	"tmp.csv"      )
			lowerCAmelCase__								=							CsvDatasetReader({"train": csv_path}						,	cache_dir=UpperCamelCase_      ).read()
			with pytest.raises(UpperCamelCase_      ):
						CsvDatasetWriter(dataset["train"]						,	UpperCamelCase_						,	num_proc=0      )







'''simple docstring'''

from __future__ import annotations

import copy
import os
import tempfile
from importlib import import_module
from math import isnan

from transformers import is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import _tf_gpu_memory_limit, require_tf, slow

from ..test_modeling_tf_common import ids_tensor


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

		from transformers import (
		    TF_MODEL_FOR_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_MASKED_LM_MAPPING,
		    TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
		    TF_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING,
		    TF_MODEL_FOR_PRETRAINING_MAPPING,
		    TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
		    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
		    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
		    TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
		    TFSharedEmbeddings,
		)

		if _tf_gpu_memory_limit is not None:
				UpperCAmelCase__   : str					=		tf.config.list_physical_devices("GPU")
				for gpu in gpus:
						# Restrict TensorFlow to only allocate x GB of memory on the GPUs
						try:
								tf.config.set_logical_device_configuration(
								    gpu, [tf.config.LogicalDeviceConfiguration(memory_limit=_tf_gpu_memory_limit)]
								)
								UpperCAmelCase__   : Optional[Any]					=		tf.config.list_logical_devices("GPU")
								print("Logical GPUs", logical_gpus)
						except RuntimeError as e:
								# Virtual devices must be set before GPUs have been initialized
								print(e)






@require_tf
class    A     :
	snake_case__      :Optional[int]        =     None
	snake_case__      :Optional[Any]        =     ()
	snake_case__      :Union[str, Any]        =     ()
	snake_case__      :Optional[int]        =     True
	snake_case__      :Any        =     True
	snake_case__      :Union[str, Any]        =     True
	snake_case__      :str        =     False





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Union[str, Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       Optional[Any]      ,							__magic_name__			:       List[str]=False							):


				"""simple docstring"""

				lowerCAmelCase__								=							copy.deepcopy(__magic_name__							)

				if model_class in get_values(__magic_name__							):
							lowerCAmelCase__								=							{
							    k: tf.tile(tf.expand_dims(__magic_name__      ,							1							)      ,							(1, self.model_tester.num_choices) + (1,) * (v.ndim - 1)							)
							    if isinstance(__magic_name__      ,							tf.Tensor							) and v.ndim > 0
							    else v
							    for k, v in inputs_dict.items()
							}

				if return_labels:
							if model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.ones(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in [
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							]:
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in get_values(__magic_name__							):
										lowerCAmelCase__								=							tf.zeros(self.model_tester.batch_size      ,							dtype=tf.intaa							)
							elif model_class in [
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							    *get_values(__magic_name__							),
							]:
										lowerCAmelCase__								=							tf.zeros(
										    (self.model_tester.batch_size, self.model_tester.seq_length)      ,							dtype=tf.intaa							)
				return inputs_dict





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)

							@tf.function
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)

							@tf.function(experimental_compile=__magic_name__							)
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							model_class(__magic_name__							)
							if getattr(__magic_name__      ,							"hf_compute_loss"      ,							__magic_name__							):
										# Test that model correctly compute the loss with kwargs
										lowerCAmelCase__								=							self._prepare_for_class(inputs_dict.copy()      ,							__magic_name__      ,							return_labels=__magic_name__							)
										# Is there a better way to remove these decoder inputs?
										lowerCAmelCase__								=							{
										    key: val
										    for key, val in prepared_for_class.items()
										    if key not in ("head_mask", "decoder_head_mask", "cross_attn_head_mask", "decoder_input_ids")
										}

										lowerCAmelCase__								=							{
										    "labels",
										    "label",
										    "label_ids",
										    "start_positions",
										    "start_position",
										    "end_positions",
										    "end_position",
										    "next_sentence_label",
										}
										lowerCAmelCase__								=							possible_label_cols.intersection(set(__magic_name__							)							)
										self.assertGreater(len(__magic_name__							)      ,							0      ,							msg="No matching label names found!"							)
										lowerCAmelCase__								=							{key: val for key, val in prepared_for_class.items() if key in label_names}
										lowerCAmelCase__								=							{key: val for key, val in prepared_for_class.items() if key not in label_names}
										self.assertGreater(len(__magic_name__							)      ,							0							)

										# Make sure it works with XLA!
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)      ,							jit_compile=__magic_name__							)
										# Make sure the model fits without crashing regardless of where we pass the labels
										lowerCAmelCase__								=							model.fit(
										    __magic_name__      ,							validation_data=__magic_name__      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=__magic_name__      ,							verbose=0      ,							)
										lowerCAmelCase__								=							history.history["loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)
										lowerCAmelCase__								=							history.history["val_loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)

										# Now test it with separate labels, to make sure that path works in XLA too.
										lowerCAmelCase__								=							model_class(__magic_name__							)
										model.compile(optimizer=tf.keras.optimizers.SGD(0.0							)      ,							jit_compile=__magic_name__							)
										lowerCAmelCase__								=							model.fit(
										    __magic_name__      ,							__magic_name__      ,							validation_data=(inputs_minus_labels, labels)      ,							steps_per_epoch=1      ,							validation_steps=1      ,							shuffle=__magic_name__      ,							verbose=0      ,							)

										lowerCAmelCase__								=							history.history["loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)
										lowerCAmelCase__								=							history.history["val_loss"][0]
										self.assertTrue(not isnan(__magic_name__							)							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       List[str]							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				lowerCAmelCase__								=							True
				lowerCAmelCase__								=							True

				if hasattr(__magic_name__      ,							"use_cache"							):
							lowerCAmelCase__								=							True

				lowerCAmelCase__								=							getattr(self.model_tester      ,							"encoder_seq_length"      ,							self.model_tester.seq_length							)
				lowerCAmelCase__								=							getattr(self.model_tester      ,							"key_length"      ,							__magic_name__							)

				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
							lowerCAmelCase__								=							model_class(__magic_name__							)
							model.build()
							lowerCAmelCase__								=							len(model(__magic_name__							)							)

							for key in list(class_inputs_dict.keys()							):
										# Remove keys not in the serving signature, as the SavedModel will not be compiled to deal with them
										if key not in model.input_signature:
													del class_inputs_dict[key]
										# Check it's a tensor, in case the inputs dict has some bools in it too
										elif isinstance(class_inputs_dict[key]      ,							tf.Tensor							) and class_inputs_dict[key].dtype.is_integer:
													lowerCAmelCase__								=							tf.cast(class_inputs_dict[key]      ,							tf.intaa							)

							if set(class_inputs_dict.keys()							) != set(model.input_signature.keys()							):
										continue  # Some models have inputs that the preparation functions don't create, we skip those

							with tempfile.TemporaryDirectory() as tmpdirname:
										model.save_pretrained(__magic_name__      ,							saved_model=__magic_name__							)
										lowerCAmelCase__								=							os.path.join(__magic_name__      ,							"saved_model"      ,							"1"							)
										lowerCAmelCase__								=							tf.keras.models.load_model(__magic_name__							)
										lowerCAmelCase__								=							model(__magic_name__							)

										if self.is_encoder_decoder:
													lowerCAmelCase__								=							outputs["encoder_hidden_states"]
													lowerCAmelCase__								=							outputs["encoder_attentions"]
										else:
													lowerCAmelCase__								=							outputs["hidden_states"]
													lowerCAmelCase__								=							outputs["attentions"]

										self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)

										lowerCAmelCase__								=							getattr(
										    self.model_tester      ,							"expected_num_hidden_layers"      ,							self.model_tester.num_hidden_layers + 1							)

										self.assertEqual(len(__magic_name__							)      ,							__magic_name__							)
										self.assertListEqual(
										    list(output_hidden_states[0].shape[-2:]							)      ,							[self.model_tester.seq_length, self.model_tester.hidden_size]      ,							)

										self.assertEqual(len(__magic_name__							)      ,							self.model_tester.num_hidden_layers							)
										self.assertListEqual(
										    list(output_attentions[0].shape[-3:]							)      ,							[self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length]      ,							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Optional[Any]							):


				"""simple docstring"""

				tf.keras.mixed_precision.set_global_policy("mixed_float16"							)

				# try/finally block to ensure subsequent tests run in float32
				try:
							lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
							for model_class in self.all_model_classes[:2]:
										lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)
										lowerCAmelCase__								=							model_class(__magic_name__							)
										lowerCAmelCase__								=							model(__magic_name__							)

										self.assertIsNotNone(__magic_name__							)
				finally:
							tf.keras.mixed_precision.set_global_policy("float32"							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()
				# head_mask and decoder_head_mask has different shapes than other input args
				if "head_mask" in inputs_dict:
							del inputs_dict["head_mask"]
				if "decoder_head_mask" in inputs_dict:
							del inputs_dict["decoder_head_mask"]
				if "cross_attn_head_mask" in inputs_dict:
							del inputs_dict["cross_attn_head_mask"]
				lowerCAmelCase__								=							{
				    module_member
				    for model_class in self.all_model_classes
				    for module in (import_module(model_class.__module__							),)
				    for module_member_name in dir(__magic_name__							)
				    if module_member_name.endswith("MainLayer"							)
				    for module_member in (getattr(__magic_name__      ,							__magic_name__							),)
				    if isinstance(__magic_name__      ,							__magic_name__							)
				    and tf.keras.layers.Layer in module_member.__bases__
				    and getattr(__magic_name__      ,							"_keras_serializable"      ,							__magic_name__							)
				}

				for main_layer_class in tf_main_layer_classes:
							# T5MainLayer needs an embed_tokens parameter when called without the inputs_embeds parameter
							if "T5" in main_layer_class.__name__:
										# Take the same values than in TFT5ModelTester for this shared layer
										lowerCAmelCase__								=							TFSharedEmbeddings(self.model_tester.vocab_size      ,							self.model_tester.hidden_size      ,							name="shared"							)
										lowerCAmelCase__								=							False
										lowerCAmelCase__								=							main_layer_class(__magic_name__      ,							embed_tokens=__magic_name__							)
							else:
										lowerCAmelCase__								=							main_layer_class(__magic_name__							)

							lowerCAmelCase__								=							{
							    name: tf.keras.Input(tensor.shape[1:]      ,							dtype=tensor.dtype							) for name, tensor in inputs_dict.items()
							}

							if hasattr(self.model_tester      ,							"num_labels"							):
										lowerCAmelCase__								=							self.model_tester.num_labels
							else:
										lowerCAmelCase__								=							2

							lowerCAmelCase__								=							tf.data.Dataset.from_tensor_slices(
							    (inputs_dict, np.ones((self.model_tester.batch_size, self.model_tester.seq_length, num_labels, 1)							))							).batch(1							)

							lowerCAmelCase__								=							main_layer(__magic_name__							)[0]
							lowerCAmelCase__								=							tf.keras.layers.Dense(__magic_name__      ,							activation="softmax"      ,							name="outputs"							)(__magic_name__							)
							lowerCAmelCase__								=							tf.keras.models.Model(inputs=__magic_name__      ,							outputs=[outputs]							)

							model.compile(loss="binary_crossentropy"      ,							optimizer="adam"      ,							metrics=["binary_accuracy"]							)
							model.fit(__magic_name__      ,							epochs=1							)

							with tempfile.TemporaryDirectory() as tmpdirname:
										lowerCAmelCase__								=							os.path.join(__magic_name__      ,							"keras_model.h5"							)
										model.save(__magic_name__							)
										if "T5" in main_layer_class.__name__:
													lowerCAmelCase__								=							tf.keras.models.load_model(
													    __magic_name__      ,							custom_objects={
													        main_layer_class.__name__: main_layer_class,
													        "TFSharedEmbeddings": TFSharedEmbeddings,
													    }      ,							)
										else:
													lowerCAmelCase__								=							tf.keras.models.load_model(
													    __magic_name__      ,							custom_objects={main_layer_class.__name__: main_layer_class}							)
										assert isinstance(__magic_name__      ,							tf.keras.Model							)
										model(__magic_name__							)





	@slow
	def 							__SCREAMING_SNAKE_CASE  (					self			:       int							):


				"""simple docstring"""

				lowerCAmelCase__	,lowerCAmelCase__								=							self.model_tester.prepare_config_and_inputs_for_common()

				for model_class in self.all_model_classes[:2]:
							lowerCAmelCase__								=							model_class(__magic_name__							)

							lowerCAmelCase__								=							copy.deepcopy(__magic_name__							)

							if not self.is_encoder_decoder:
										lowerCAmelCase__								=							inputs["input_ids"]
										del inputs["input_ids"]
							else:
										lowerCAmelCase__								=							inputs["input_ids"]
										lowerCAmelCase__								=							inputs.get("decoder_input_ids"      ,							__magic_name__							)
										del inputs["input_ids"]
										inputs.pop("decoder_input_ids"      ,							__magic_name__							)

							if not self.is_encoder_decoder:
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)
							else:
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)
										lowerCAmelCase__								=							model.get_input_embeddings()(__magic_name__							)

							lowerCAmelCase__								=							self._prepare_for_class(__magic_name__      ,							__magic_name__							)

							@tf.function
							def run_in_graph_mode():
										return model(__magic_name__							)

							lowerCAmelCase__								=							run_in_graph_mode()
							self.assertIsNotNone(__magic_name__							)





	def 							__SCREAMING_SNAKE_CASE  (					self			:       int      ,							__magic_name__			:       int      ,							__magic_name__			:       Any							):


				"""simple docstring"""

				lowerCAmelCase__								=							[]
				if model.config.bos_token_id is not None:
							special_tokens.append(model.config.bos_token_id							)
				if model.config.pad_token_id is not None:
							special_tokens.append(model.config.pad_token_id							)
				if model.config.eos_token_id is not None:
							special_tokens.append(model.config.eos_token_id							)

				# create random bad tokens that are not special tokens
				lowerCAmelCase__								=							[]
				while len(__magic_name__							) < num_bad_tokens:
							lowerCAmelCase__								=							tf.squeeze(ids_tensor((1, 1)      ,							self.model_tester.vocab_size							)      ,							0							).numpy()[0]
							if token not in special_tokens:
										bad_tokens.append(__magic_name__							)
				return bad_tokens





	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       Tuple							):


				"""simple docstring"""

				for token_id in output_ids[0].numpy().tolist():
							self.assertGreaterEqual(__magic_name__      ,							0							)
							self.assertLess(__magic_name__      ,							self.model_tester.vocab_size							)







	def 							__SCREAMING_SNAKE_CASE  (					self			:       Tuple      ,							__magic_name__			:       str      ,							__magic_name__			:       Dict							):


				"""simple docstring"""

				for bad_word_ids in bad_words_ids:
							# for all slices in batch
							for generated_ids_slice in generated_ids:
										# for all word idx
										for i in range(len(__magic_name__							)      ,							len(__magic_name__							)							):
													# if tokens match
													if generated_ids_slice[i - len(__magic_name__							) : i] == bad_word_ids:
																return True
				return False





