






'''simple docstring'''


import argparse
import glob
import inspect
import os
import re
import unittest

from get_test_info import get_test_classes

from tests.test_pipeline_mixin import pipeline_test_mapping


lowerCamelCase_					:			str     						=				{}
for task, _ in pipeline_test_mapping.items():
					lowerCamelCase_					:			str     						=				{'''pt''': None, '''tf''': None}


# DO **NOT** add item to this set (unless the reason is approved)
lowerCamelCase_					:			int     						=				{
    '''tests/models/esm/test_modeling_esmfold.py''',  # The pipeline test mapping is added to `test_modeling_esm.py`
}







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						if "ModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "pt"
						elif "TFModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "tf"
						elif "FlaxModelTesterMixin" in [x.__name__ for x in test_class.__bases__]:
												return "flax"
						else:
												return None







def 						__magic_name__(			_A    ,   _A       ):
						'''simple docstring'''





						if PIPELINE_TEST_MAPPING[task].get(_A    ,   _A       ) is not None:
												return PIPELINE_TEST_MAPPING[task][framework]

						UpperCamelCase__       =			pipeline_test_mapping[task]["""test"""]
						UpperCamelCase__       =			None

						if framework == "pt":
												UpperCamelCase__       =			getattr(_A    ,   """model_mapping"""    ,   _A       )
						elif framework == "tf":
												UpperCamelCase__       =			getattr(_A    ,   """tf_model_mapping"""    ,   _A       )

						if mapping is not None:
												UpperCamelCase__       =			dict(mapping.items()       )

						# cache the results
						UpperCamelCase__       =			mapping
						return mapping







def 						__magic_name__(			_A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			get_framework(_A       )
						if framework is None:
												return None
						UpperCamelCase__       =			get_mapping_for_task(_A    ,   _A       )
						if mapping is None:
												return None

						UpperCamelCase__       =			list({model_class.config_class for model_class in test_class.all_model_classes}       )
						if len(_A       ) != 1:
												raise ValueError("""There should be exactly one configuration class from `test_class.all_model_classes`."""       )

						# This could be a list/tuple of model classes, but it's rare.
						UpperCamelCase__       =			mapping.get(config_classes[0]    ,   _A       )
						if isinstance(_A    ,   (tuple, list)       ):
												UpperCamelCase__       =			sorted(_A    ,   key=lambda _A       : x.__name__       )

						return model_class







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			[(task, get_model_for_pipeline_test(_A    ,   _A       )) for task in pipeline_test_mapping]
						UpperCamelCase__       =			sorted([(task, model) for task, model in mapping if model is not None]    ,   key=lambda _A       : x[0]       )

						return dict(_A       )







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			get_framework(_A       )
						if framework == "pt":
												UpperCamelCase__       =			"""torch"""
						UpperCamelCase__       =			"""{}"""

						UpperCamelCase__       =			get_pipeline_model_mapping(_A       )
						if len(_A       ) == 0:
												return ""

						UpperCamelCase__       =			[]
						for task, model_classes in mapping.items():
												if isinstance(_A    ,   (tuple, list)       ):
																		# A list/tuple of model classes
																		UpperCamelCase__       =			"""(""" + """, """.join([x.__name__ for x in model_classes]       ) + """)"""
												else:
																		# A single model class
																		UpperCamelCase__       =			model_classes.__name__
												texts.append(f"\"{task}\": {value}"       )
						UpperCamelCase__       =			"""{""" + """, """.join(_A       ) + """}"""
						UpperCamelCase__       =			f"pipeline_model_mapping = {text} if is_{framework}_available() else {default_value}"

						return text







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			{"""ModelTesterMixin""", """TFModelTesterMixin""", """FlaxModelTesterMixin"""}
						if not issubclass(_A    ,   unittest.TestCase       ):
												return False
						return len(base_class_names.intersection([x.__name__ for x in test_class.__bases__]       )       ) > 0







def 						__magic_name__(			_A       ):
						'''simple docstring'''





						UpperCamelCase__       =			[x for x in get_test_classes(_A       ) if is_valid_test_class(_A       )]

						UpperCamelCase__       =			None
						for test_class in test_classes:
												# If a test class has defined `pipeline_model_mapping`, let's take it
												if getattr(_A    ,   """pipeline_model_mapping"""    ,   _A       ) is not None:
																		UpperCamelCase__       =			test_class
																		break
    # Take the test class with the shortest name (just a heuristic)
						if target_test_class is None and len(_A       ) > 0:
												UpperCamelCase__       =			sorted(_A    ,   key=lambda _A       : (len(x.__name__       ), x.__name__)       )[0]

						return target_test_class







def 						__magic_name__(			_A    ,   _A    ,   _A       ):
						'''simple docstring'''





						UpperCamelCase__       =			start_idx
						for idx, line in enumerate(lines[start_idx:]       ):
												UpperCamelCase__       =			len(_A       ) - len(line.lstrip()       )
												if idx == 0 or indent > indent_level or (indent == indent_level and line.strip() == ")"):
																		UpperCamelCase__       =			start_idx + idx
												elif idx > 0 and indent <= indent_level:
																		# Outside the definition block of `pipeline_model_mapping`
																		break

						return end_idx







def 						__magic_name__(			_A    ,   _A=False       ):
						'''simple docstring'''





						if getattr(_A    ,   """pipeline_model_mapping"""    ,   _A       ) is not None:
												if not overwrite:
																		return "", -1

						UpperCamelCase__       =			get_pipeline_model_mapping_string(_A       )
						if len(_A       ) == 0:
												return "", -1
						UpperCamelCase__       =			line_to_add + """\n"""

						# The code defined the class `test_class`
						UpperCamelCase__     , UpperCamelCase__       =			inspect.getsourcelines(_A       )
						# `inspect` gives the code for an object, including decorator(s) if any.
						# We (only) need the exact line of the class definition.
						for idx, line in enumerate(_A       ):
												if line.lstrip().startswith("""class """       ):
																		UpperCamelCase__       =			class_lines[idx:]
																		class_start_line_no += idx
																		break
						UpperCamelCase__       =			class_start_line_no + len(_A       ) - 1

						# The index in `class_lines` that starts the definition of `all_model_classes`, `all_generative_model_classes` or
						# `pipeline_model_mapping`. This assumes they are defined in such order, and we take the start index of the last
						# block that appears in a `test_class`.
						UpperCamelCase__       =			None
						# The indent level of the line at `class_lines[start_idx]` (if defined)
						UpperCamelCase__       =			0
						# To record if `pipeline_model_mapping` is found in `test_class`.
						UpperCamelCase__       =			None
						for idx, line in enumerate(_A       ):
												if line.strip().startswith("""all_model_classes = """       ):
																		UpperCamelCase__       =			len(_A       ) - len(line.lstrip()       )
																		UpperCamelCase__       =			idx
												elif line.strip().startswith("""all_generative_model_classes = """       ):
																		UpperCamelCase__       =			len(_A       ) - len(line.lstrip()       )
																		UpperCamelCase__       =			idx
												elif line.strip().startswith("""pipeline_model_mapping = """       ):
																		UpperCamelCase__       =			len(_A       ) - len(line.lstrip()       )
																		UpperCamelCase__       =			idx
																		UpperCamelCase__       =			line
																		break

						if start_idx is None:
												return "", -1
						# Find the ending index (inclusive) of the above found block.
						UpperCamelCase__       =			find_block_ending(_A    ,   _A    ,   _A       )

						# Extract `is_xxx_available()` from existing blocks: some models require specific libraries like `timm` and use
						# `is_timm_available()` instead of `is_torch_available()`.
						# Keep leading and trailing whitespaces
						UpperCamelCase__       =			re.compile(r"""\s(is_\S+?_available\(\))\s"""       )
						for line in class_lines[start_idx : end_idx + 1]:
												UpperCamelCase__       =			r.search(_A       )
												if backend_condition is not None:
																		# replace the leading and trailing whitespaces to the space character " ".
																		UpperCamelCase__       =			""" """ + backend_condition[0][1:-1] + """ """
																		UpperCamelCase__       =			r.sub(_A    ,   _A       )
																		break

						if def_line is None:
												# `pipeline_model_mapping` is not defined. The target index is set to the ending index (inclusive) of
												# `all_model_classes` or `all_generative_model_classes`.
												UpperCamelCase__       =			end_idx
						else:
												# `pipeline_model_mapping` is defined. The target index is set to be one **BEFORE** its start index.
												UpperCamelCase__       =			start_idx - 1
												# mark the lines of the currently existing `pipeline_model_mapping` to be removed.
												for idx in range(_A    ,   end_idx + 1       ):
																		# These lines are going to be removed before writing to the test file.
																		UpperCamelCase__       =			None  # noqa

    # Make sure the test class is a subclass of `PipelineTesterMixin`.
						UpperCamelCase__       =			[x.__name__ for x in test_class.__bases__]
						if "PipelineTesterMixin" not in parent_classes:
												# Put `PipelineTesterMixin` just before `unittest.TestCase`
												UpperCamelCase__       =			[x for x in parent_classes if x != """TestCase"""] + ["""PipelineTesterMixin"""]
												if "TestCase" in parent_classes:
																		# Here we **assume** the original string is always with `unittest.TestCase`.
																		_parent_classes.append("""unittest.TestCase"""       )
												UpperCamelCase__       =			""", """.join(_parent_classes       )
												for idx, line in enumerate(_A       ):
																		# Find the ending of the declaration of `test_class`
																		if line.strip().endswith("""):"""       ):
																								# mark the lines of the declaration of `test_class` to be removed
																								for _idx in range(idx + 1       ):
																														UpperCamelCase__       =			None  # noqa
																								break
        # Add the new, one-line, class declaration for `test_class`
												UpperCamelCase__       =			f"class {test_class.__name__}({parent_classes}):\n"

						# Add indentation
						UpperCamelCase__       =			""" """ * indent_level + line_to_add
						# Insert `pipeline_model_mapping` to `class_lines`.
						# (The line at `target_idx` should be kept by definition!)
						UpperCamelCase__       =			class_lines[: target_idx + 1] + [line_to_add] + class_lines[target_idx + 1 :]
						# Remove the lines that are marked to be removed
						UpperCamelCase__       =			[x for x in class_lines if x is not None]

						# Move from test class to module (in order to write to the test file)
						UpperCamelCase__       =			inspect.getsourcelines(inspect.getmodule(_A       )       )[0]
						# Be careful with the 1-off between line numbers and array indices
						UpperCamelCase__       =			module_lines[: class_start_line_no - 1] + class_lines + module_lines[class_end_line_no:]
						UpperCamelCase__       =			"""""".join(_A       )

						UpperCamelCase__       =			inspect.getsourcefile(_A       )
						with open(_A    ,   """w"""    ,   encoding="""UTF-8"""    ,   newline="""\n"""       ) as fp:
												fp.write(_A       )

						return line_to_add







def 						__magic_name__(			_A    ,   _A=False       ):
						'''simple docstring'''





						UpperCamelCase__       =			find_test_class(_A       )
						if test_class:
												add_pipeline_model_mapping(_A    ,   overwrite=_A       )


if __name__ == "__main__":
					lowerCamelCase_					:			List[str]     						=				argparse.ArgumentParser()
					parser.add_argument(
					    '''--test_file''', type=str, help='''A path to the test file, starting with the repository\'s `tests` directory.'''
					)
					parser.add_argument(
					    '''--all''',
					    action='''store_true''',
					    help='''If to check and modify all test files.''',
					)
					parser.add_argument(
					    '''--overwrite''',
					    action='''store_true''',
					    help='''If to overwrite a test class if it has already defined `pipeline_model_mapping`.''',
					)
					lowerCamelCase_					:			str     						=				parser.parse_args()

					if not args.all and not args.test_file:
										raise ValueError('''Please specify either `test_file` or pass `--all` to check/modify all test files.''')
					elif args.all and args.test_file:
										raise ValueError('''Only one of `--test_file` and `--all` could be specified.''')

					lowerCamelCase_					:			int     						=				[]
					if args.test_file:
										lowerCamelCase_					:			Optional[Any]     						=				[args.test_file]
					else:
										lowerCamelCase_					:			Union[str, Any]     						=				os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
										for test_file in glob.glob(pattern):
															# `Flax` is not concerned at this moment
															if not test_file.startswith('''test_modeling_flax_'''):
																				test_files.append(test_file)

					for test_file in test_files:
										if test_file in TEST_FILE_TO_IGNORE:
															print(f"""[SKIPPED] {test_file} is skipped as it is in `TEST_FILE_TO_IGNORE` in the file {__file__}.""")
															continue
										add_pipeline_model_mapping_to_test_file(test_file, overwrite=args.overwrite)








'''simple docstring'''


import json
import os
import shutil
import tempfile
import unittest

from transformers import is_speech_available, is_torch_available
from transformers.models.speechta import SpeechTaTokenizer
from transformers.testing_utils import get_tests_dir, require_torch
from transformers.utils import FEATURE_EXTRACTOR_NAME


if is_speech_available() and is_torch_available():
					from transformers import SpeechTaFeatureExtractor, SpeechTaProcessor

					from .test_feature_extraction_speechta import floats_list


lowerCamelCase_					:			List[str]     						=				get_tests_dir('''fixtures/test_sentencepiece_bpe_char.model''')
@require_torch
class  _SCREAMING_SNAKE_CASE				(		unittest.TestCase						):


			'''simple docstring'''





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

									'''simple docstring'''







									UpperCamelCase__       =			tempfile.mkdtemp()

									UpperCamelCase__       =			SpeechTaTokenizer(lowercase      )
									tokenizer.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			{
									    """feature_size""": 1,
									    """padding_value""": 0.0,
									    """sampling_rate""": 1_6_0_0_0,
									    """do_normalize""": False,
									    """num_mel_bins""": 8_0,
									    """hop_length""": 1_6,
									    """win_length""": 6_4,
									    """win_function""": """hann_window""",
									    """fmin""": 8_0,
									    """fmax""": 7_6_0_0,
									    """mel_floor""": 1e-10,
									    """reduction_factor""": 2,
									    """return_attention_mask""": True,
									}

									UpperCamelCase__       =			os.path.join(self.tmpdirname			,   lowercase      )
									with open(self.feature_extraction_file			,   """w"""			,   encoding="""utf-8"""      ) as fp:
															fp.write(json.dumps(lowercase      ) + """\n"""      )






			def 					A   ( self				:    Union[str, Any]			,   **lowercase				:    List[Any]      )				->     Any:

									'''simple docstring'''







									return SpeechTaTokenizer.from_pretrained(self.tmpdirname			,   **lowercase      )






			def 					A   ( self				:    str			,   **lowercase				:    Tuple      )				->     Optional[Any]:

									'''simple docstring'''







									return SpeechTaFeatureExtractor.from_pretrained(self.tmpdirname			,   **lowercase      )






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

									'''simple docstring'''







									shutil.rmtree(self.tmpdirname      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.get_tokenizer()
									UpperCamelCase__       =			self.get_feature_extractor()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									processor.save_pretrained(self.tmpdirname      )
									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(self.tmpdirname      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






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

									'''simple docstring'''







									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=self.get_tokenizer()			,   feature_extractor=self.get_feature_extractor()      )
									processor.save_pretrained(self.tmpdirname      )

									UpperCamelCase__       =			self.get_tokenizer(bos_token="""(BOS)"""			,   eos_token="""(EOS)"""      )
									UpperCamelCase__       =			self.get_feature_extractor(do_normalize=lowercase			,   padding_value=1.0      )

									UpperCamelCase__       =			SpeechTaProcessor.from_pretrained(
									    self.tmpdirname			,   bos_token="""(BOS)"""			,   eos_token="""(EOS)"""			,   do_normalize=lowercase			,   padding_value=1.0      )

									self.assertEqual(processor.tokenizer.get_vocab()			,   tokenizer_add_kwargs.get_vocab()      )
									self.assertIsInstance(processor.tokenizer			,   lowercase      )

									self.assertEqual(processor.feature_extractor.to_json_string()			,   feature_extractor_add_kwargs.to_json_string()      )
									self.assertIsInstance(processor.feature_extractor			,   lowercase      )






			def 					A   ( self				:    List[Any]      )				->     Dict:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			floats_list((3, 1_0_0_0)      )

									UpperCamelCase__       =			feature_extractor(audio_target=lowercase			,   return_tensors="""np"""      )
									UpperCamelCase__       =			processor(audio_target=lowercase			,   return_tensors="""np"""      )

									for key in input_feat_extract.keys():
															self.assertAlmostEqual(input_feat_extract[key].sum()			,   input_processor[key].sum()			,   delta=1e-2      )






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

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    List[Any]      )				->     int:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			"""This is a test string"""

									UpperCamelCase__       =			processor(text_target=lowercase      )
									UpperCamelCase__       =			tokenizer(lowercase      )

									for key in encoded_tok.keys():
															self.assertListEqual(encoded_tok[key]			,   encoded_processor[key]      )






			def 					A   ( self				:    Any      )				->     Tuple:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									UpperCamelCase__       =			[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

									UpperCamelCase__       =			processor.batch_decode(lowercase      )
									UpperCamelCase__       =			tokenizer.batch_decode(lowercase      )

									self.assertListEqual(lowercase			,   lowercase      )







			def 					A   ( self				:    List[Any]      )				->     List[str]:

									'''simple docstring'''







									UpperCamelCase__       =			self.get_feature_extractor()
									UpperCamelCase__       =			self.get_tokenizer()

									UpperCamelCase__       =			SpeechTaProcessor(tokenizer=lowercase			,   feature_extractor=lowercase      )

									self.assertListEqual(
									    processor.model_input_names			,   feature_extractor.model_input_names			,   msg="""`processor` and `feature_extractor` model input names do not match"""			,   )
