




"""simple docstring"""




from __future__ import annotations

import unittest

from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property

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


if is_tf_available():
     import tensorflow as tf

     from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel





@require_tf
class 			__lowerCamelCase	:




    """simple docstring"""

    snake_case__ =				PegasusConfig
    snake_case__ =				{}
    snake_case__ =				"gelu"


    def __init__(	self					:					List[Any]       ,     SCREAMING_SNAKE_CASE__					:					str       ,     SCREAMING_SNAKE_CASE__					:					Any=13       ,     SCREAMING_SNAKE_CASE__					:					Optional[Any]=7       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=True       ,     SCREAMING_SNAKE_CASE__					:					List[Any]=False       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=99       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=32       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]=2       ,     SCREAMING_SNAKE_CASE__					:					Dict=4       ,     SCREAMING_SNAKE_CASE__					:					List[str]=37       ,     SCREAMING_SNAKE_CASE__					:					Any=0.1       ,     SCREAMING_SNAKE_CASE__					:					int=0.1       ,     SCREAMING_SNAKE_CASE__					:					Dict=40       ,     SCREAMING_SNAKE_CASE__					:					str=2       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]=1       ,     SCREAMING_SNAKE_CASE__					:					Any=0       ,     )				->	Optional[Any]:
         lowerCAmelCase__          =			parent
         lowerCAmelCase__          =			batch_size
         lowerCAmelCase__          =			seq_length
         lowerCAmelCase__          =			is_training
         lowerCAmelCase__          =			use_labels
         lowerCAmelCase__          =			vocab_size
         lowerCAmelCase__          =			hidden_size
         lowerCAmelCase__          =			num_hidden_layers
         lowerCAmelCase__          =			num_attention_heads
         lowerCAmelCase__          =			intermediate_size

         lowerCAmelCase__          =			hidden_dropout_prob
         lowerCAmelCase__          =			attention_probs_dropout_prob
         lowerCAmelCase__          =			max_position_embeddings
         lowerCAmelCase__          =			eos_token_id
         lowerCAmelCase__          =			pad_token_id
         lowerCAmelCase__          =			bos_token_id


    def        a				(	self					:					str       )				->	Any:
         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length - 1]       ,     self.vocab_size       )
         lowerCAmelCase__          =			tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size       )       ,     1       )
         lowerCAmelCase__          =			tf.concat([input_ids, eos_tensor]       ,     axis=1       )

         lowerCAmelCase__          =			ids_tensor([self.batch_size, self.seq_length]       ,     self.vocab_size       )

         lowerCAmelCase__          =			self.config_cls(
             vocab_size=self.vocab_size       ,     d_model=self.hidden_size       ,     encoder_layers=self.num_hidden_layers       ,     decoder_layers=self.num_hidden_layers       ,     encoder_attention_heads=self.num_attention_heads       ,     decoder_attention_heads=self.num_attention_heads       ,     encoder_ffn_dim=self.intermediate_size       ,     decoder_ffn_dim=self.intermediate_size       ,     dropout=self.hidden_dropout_prob       ,     attention_dropout=self.attention_probs_dropout_prob       ,     max_position_embeddings=self.max_position_embeddings       ,     eos_token_ids=[2]       ,     bos_token_id=self.bos_token_id       ,     pad_token_id=self.pad_token_id       ,     decoder_start_token_id=self.pad_token_id       ,     **self.config_updates       ,     )
         lowerCAmelCase__          =			prepare_pegasus_inputs_dict(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         return config, inputs_dict


    def        a				(	self					:					Optional[Any]       ,     SCREAMING_SNAKE_CASE__					:					Union[str, Any]       ,     SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Optional[Any]:
         lowerCAmelCase__          =			TFPegasusModel(config=SCREAMING_SNAKE_CASE__       ).get_decoder()
         lowerCAmelCase__          =			inputs_dict["input_ids"]

         lowerCAmelCase__          =			input_ids[:1, :]
         lowerCAmelCase__          =			inputs_dict["attention_mask"][:1, :]
         lowerCAmelCase__          =			inputs_dict["head_mask"]
         lowerCAmelCase__          =			1

         # first forward pass
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     head_mask=SCREAMING_SNAKE_CASE__       ,     use_cache=SCREAMING_SNAKE_CASE__       )

         lowerCAmelCase__     ,		lowerCAmelCase__          =			outputs.to_tuple()

         # create hypothetical next token and extent to next_input_ids
         lowerCAmelCase__          =			ids_tensor((self.batch_size, 3)       ,     config.vocab_size       )
         lowerCAmelCase__          =			tf.cast(ids_tensor((self.batch_size, 3)       ,     2       )       ,     tf.inta       )

         # append to next input_ids and
         lowerCAmelCase__          =			tf.concat([input_ids, next_tokens]       ,     axis=-1       )
         lowerCAmelCase__          =			tf.concat([attention_mask, next_attn_mask]       ,     axis=-1       )

         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       )[0]
         lowerCAmelCase__          =			model(SCREAMING_SNAKE_CASE__       ,     attention_mask=SCREAMING_SNAKE_CASE__       ,     past_key_values=SCREAMING_SNAKE_CASE__       )[0]

         self.parent.assertEqual(next_tokens.shape[1]       ,     output_from_past.shape[1]       )

         # select random slice
         lowerCAmelCase__          =			int(ids_tensor((1,)       ,     output_from_past.shape[-1]       )       )
         lowerCAmelCase__          =			output_from_no_past[:, -3:, random_slice_idx]
         lowerCAmelCase__          =			output_from_past[:, :, random_slice_idx]

         # test that outputs are equal for slice
         tf.debugging.assert_near(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       ,     rtol=1e-3       )






def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: Optional[Any]       ,    lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: str=None       ,    lowerCAmelCase_					: List[str]=None       ,    lowerCAmelCase_					: Tuple=None       ,    lowerCAmelCase_					: Tuple=None       ,    lowerCAmelCase_					: List[str]=None       ,    ):
     """simple docstring"""

     if attention_mask is None:
          lowerCAmelCase__          =			tf.cast(tf.math.not_equal(lowerCAmelCase_       ,    config.pad_token_id      )       ,    tf.inta      )
     if decoder_attention_mask is None:
          lowerCAmelCase__          =			tf.concat(
              [
                  tf.ones(decoder_input_ids[:, :1].shape       ,    dtype=tf.inta      ),
                  tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:]       ,    config.pad_token_id      )       ,    tf.inta      ),
              ]       ,    axis=-1       ,    )
     if head_mask is None:
          lowerCAmelCase__          =			tf.ones((config.encoder_layers, config.encoder_attention_heads)      )
     if decoder_head_mask is None:
          lowerCAmelCase__          =			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     if cross_attn_head_mask is None:
          lowerCAmelCase__          =			tf.ones((config.decoder_layers, config.decoder_attention_heads)      )
     return {
         "input_ids": input_ids,
         "decoder_input_ids": decoder_input_ids,
         "attention_mask": attention_mask,
         "decoder_attention_mask": decoder_attention_mask,
         "head_mask": head_mask,
         "decoder_head_mask": decoder_head_mask,
         "cross_attn_head_mask": cross_attn_head_mask,
     }





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




    """simple docstring"""

    snake_case__ =				(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
    snake_case__ =				(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
    snake_case__ =				(
        {
            "conversational": TFPegasusForConditionalGeneration,
            "feature-extraction": TFPegasusModel,
            "summarization": TFPegasusForConditionalGeneration,
            "text2text-generation": TFPegasusForConditionalGeneration,
            "translation": TFPegasusForConditionalGeneration,
        }
        if is_tf_available()
        else {}
    )
    snake_case__ =				True
    snake_case__ =				False
    snake_case__ =				False


    def        a				(	self					:					Optional[int]       )				->	List[str]:
         lowerCAmelCase__          =			TFPegasusModelTester(self       )
         lowerCAmelCase__          =			ConfigTester(self       ,     config_class=SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Tuple       )				->	Tuple:
         self.config_tester.run_common_tests()


    def        a				(	self					:					List[Any]       )				->	Optional[Any]:
         lowerCAmelCase__          =			self.model_tester.prepare_config_and_inputs_for_common()
         self.model_tester.check_decoder_model_past_large_inputs(*SCREAMING_SNAKE_CASE__       )






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




    """simple docstring"""

    snake_case__ =				[
        " PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.",
        " The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a  re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" ",
    ]
    snake_case__ =				[
        "California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"
        " reduce the risk of wildfires.",
        "N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.",
    ]  # differs slightly from pytorch, likely due to numerical differences in linear layers
    snake_case__ =				"google/pegasus-xsum"


    @cached_property
    def        a				(	self					:					Union[str, Any]       )				->	List[str]:
         return AutoTokenizer.from_pretrained(self.model_name       )


    @cached_property
    def        a				(	self					:					int       )				->	Optional[Any]:
         lowerCAmelCase__          =			TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name       )
         return model


    def        a				(	self					:					int       ,     **SCREAMING_SNAKE_CASE__					:					Optional[Any]       )				->	Tuple:
         lowerCAmelCase__          =			self.translate_src_text(**SCREAMING_SNAKE_CASE__       )
         assert self.expected_text == generated_words


    def        a				(	self					:					List[Any]       ,     **SCREAMING_SNAKE_CASE__					:					str       )				->	Dict:
         lowerCAmelCase__          =			self.tokenizer(self.src_text       ,     **SCREAMING_SNAKE_CASE__       ,     padding=SCREAMING_SNAKE_CASE__       ,     return_tensors="tf"       )
         lowerCAmelCase__          =			self.model.generate(
             model_inputs.input_ids       ,     attention_mask=model_inputs.attention_mask       ,     num_beams=2       ,     use_cache=SCREAMING_SNAKE_CASE__       ,     )
         lowerCAmelCase__          =			self.tokenizer.batch_decode(generated_ids.numpy()       ,     skip_special_tokens=SCREAMING_SNAKE_CASE__       )
         return generated_words


    @slow
    def        a				(	self					:					List[Any]       )				->	Tuple:
         self._assert_generated_batch_equal_expected()






import argparse
import glob
import importlib.util
import os
import re

import black
from doc_builder.style_doc import style_docstrings_in_code


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
UpperCamelCase								=					'src/diffusers'
UpperCamelCase								=					'.'


# This is to make sure the diffusers module imported is the one in the repo.
UpperCamelCase								=					importlib.util.spec_from_file_location(
    'diffusers',
    os.path.join(DIFFUSERS_PATH, '__init__.py'),
    submodule_search_locations=[DIFFUSERS_PATH],
)
UpperCamelCase								=					spec.loader.load_module()



def 							_A       (						lowerCAmelCase_					: Tuple       ,    lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

     return line.startswith(lowerCAmelCase_      ) or len(lowerCAmelCase_      ) <= 1 or re.search(r"^\s*\)(\s*->.*:|:)\s*$"       ,    lowerCAmelCase_      ) is not None



def 							_A       (						lowerCAmelCase_					: List[str]      ):
     """simple docstring"""

     lowerCAmelCase__          =			object_name.split("."      )
     lowerCAmelCase__          =			0

     # First let's find the module where our object lives.
     lowerCAmelCase__          =			parts[i]
     while i < len(lowerCAmelCase_      ) and not os.path.isfile(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )      ):
          i += 1
          if i < len(lowerCAmelCase_      ):
               lowerCAmelCase__          =			os.path.join(lowerCAmelCase_       ,    parts[i]      )
     if i >= len(lowerCAmelCase_      ):
          raise ValueError(F'`object_name` should begin with the name of a module of diffusers but got {object_name}.'      )

     with open(os.path.join(lowerCAmelCase_       ,    F'{module}.py'      )       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()

     # Now let's find the class / func in the code!
     lowerCAmelCase__          =			""
     lowerCAmelCase__          =			0
     for name in parts[i + 1 :]:
          while (
              line_index < len(lowerCAmelCase_      ) and re.search(rF'^{indent}(class|def)\s+{name}(\(|\:)'       ,    lines[line_index]      ) is None
          ):
               line_index += 1
          indent += "    "
          line_index += 1

     if line_index >= len(lowerCAmelCase_      ):
          raise ValueError(F' {object_name} does not match any function or class in {module}.'      )

     # We found the beginning of the class / func, now let's find the end (when the indent diminishes).
     lowerCAmelCase__          =			line_index
     while line_index < len(lowerCAmelCase_      ) and _should_continue(lines[line_index]       ,    lowerCAmelCase_      ):
          line_index += 1
     # Clean up empty lines at the end (if any).
     while len(lines[line_index - 1]      ) <= 1:
          line_index -= 1

     lowerCAmelCase__          =			lines[start_index:line_index]
     return "".join(lowerCAmelCase_      )


UpperCamelCase								=					re.compile(R'^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)')
UpperCamelCase								=					re.compile(R'^\s*(\S+)->(\S+)(\s+.*|$)')
UpperCamelCase								=					re.compile(R'<FILL\s+[^>]*>')



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			code.split("\n"      )
     lowerCAmelCase__          =			0
     while idx < len(lowerCAmelCase_      ) and len(lines[idx]      ) == 0:
          idx += 1
     if idx < len(lowerCAmelCase_      ):
          return re.search(r"^(\s*)\S"       ,    lines[idx]      ).groups()[0]
     return ""



def 							_A       (						lowerCAmelCase_					: int      ):
     """simple docstring"""

     lowerCAmelCase__          =			len(get_indent(lowerCAmelCase_      )      ) > 0
     if has_indent:
          lowerCAmelCase__          =			F'class Bla:\n{code}'
     lowerCAmelCase__          =			black.Mode(target_versions={black.TargetVersion.PYaa}       ,    line_length=119       ,    preview=lowerCAmelCase_      )
     lowerCAmelCase__          =			black.format_str(lowerCAmelCase_       ,    mode=lowerCAmelCase_      )
     lowerCAmelCase__     ,		lowerCAmelCase__          =			style_docstrings_in_code(lowerCAmelCase_      )
     return result[len("class Bla:\n"      ) :] if has_indent else result



def 							_A       (						lowerCAmelCase_					: Optional[int]       ,    lowerCAmelCase_					: List[Any]=False      ):
     """simple docstring"""

     with open(lowerCAmelCase_       ,    "r"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
          lowerCAmelCase__          =			f.readlines()
     lowerCAmelCase__          =			[]
     lowerCAmelCase__          =			0
     # Not a for loop cause `lines` is going to change (if `overwrite=True`).
     while line_index < len(lowerCAmelCase_      ):
          lowerCAmelCase__          =			_re_copy_warning.search(lines[line_index]      )
          if search is None:
               line_index += 1
               continue

          # There is some copied code here, let's retrieve the original.
          lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			search.groups()
          lowerCAmelCase__          =			find_code_in_diffusers(lowerCAmelCase_      )
          lowerCAmelCase__          =			get_indent(lowerCAmelCase_      )

          lowerCAmelCase__          =			line_index + 1 if indent == theoretical_indent else line_index + 2
          lowerCAmelCase__          =			theoretical_indent
          lowerCAmelCase__          =			start_index

          # Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
          lowerCAmelCase__          =			True
          while line_index < len(lowerCAmelCase_      ) and should_continue:
               line_index += 1
               if line_index >= len(lowerCAmelCase_      ):
                    break
               lowerCAmelCase__          =			lines[line_index]
               lowerCAmelCase__          =			_should_continue(lowerCAmelCase_       ,    lowerCAmelCase_      ) and re.search(F'^{indent}# End copy'       ,    lowerCAmelCase_      ) is None
          # Clean up empty lines at the end (if any).
          while len(lines[line_index - 1]      ) <= 1:
               line_index -= 1

          lowerCAmelCase__          =			lines[start_index:line_index]
          lowerCAmelCase__          =			"".join(lowerCAmelCase_      )

          # Remove any nested `Copied from` comments to avoid circular copies
          lowerCAmelCase__          =			[line for line in theoretical_code.split("\n"      ) if _re_copy_warning.search(lowerCAmelCase_      ) is None]
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )

          # Before comparing, use the `replace_pattern` on the original code.
          if len(lowerCAmelCase_      ) > 0:
               lowerCAmelCase__          =			replace_pattern.replace("with"       ,    ""      ).split(","      )
               lowerCAmelCase__          =			[_re_replace_pattern.search(lowerCAmelCase_      ) for p in patterns]
               for pattern in patterns:
                    if pattern is None:
                         continue
                    lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__          =			pattern.groups()
                    lowerCAmelCase__          =			re.sub(lowerCAmelCase_       ,    lowerCAmelCase_       ,    lowerCAmelCase_      )
                    if option.strip() == "all-casing":
                         lowerCAmelCase__          =			re.sub(obja.lower()       ,    obja.lower()       ,    lowerCAmelCase_      )
                         lowerCAmelCase__          =			re.sub(obja.upper()       ,    obja.upper()       ,    lowerCAmelCase_      )

            # Blackify after replacement. To be able to do that, we need the header (class or function definition)
            # from the previous line
               lowerCAmelCase__          =			blackify(lines[start_index - 1] + theoretical_code      )
               lowerCAmelCase__          =			theoretical_code[len(lines[start_index - 1]      ) :]

          # Test for a diff and act accordingly.
          if observed_code != theoretical_code:
               diffs.append([object_name, start_index]      )
               if overwrite:
                    lowerCAmelCase__          =			lines[:start_index] + [theoretical_code] + lines[line_index:]
                    lowerCAmelCase__          =			start_index + 1

     if overwrite and len(lowerCAmelCase_      ) > 0:
          # Warn the user a file has been modified.
          print(F'Detected changes, rewriting {filename}.'      )
          with open(lowerCAmelCase_       ,    "w"       ,    encoding="utf-8"       ,    newline="\n"      ) as f:
               f.writelines(lowerCAmelCase_      )
     return diffs



def 							_A       (						lowerCAmelCase_					: bool = False      ):
     """simple docstring"""

     lowerCAmelCase__          =			glob.glob(os.path.join(lowerCAmelCase_       ,    "**/*.py"      )       ,    recursive=lowerCAmelCase_      )
     lowerCAmelCase__          =			[]
     for filename in all_files:
          lowerCAmelCase__          =			is_copy_consistent(lowerCAmelCase_       ,    lowerCAmelCase_      )
          diffs += [F'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
     if not overwrite and len(lowerCAmelCase_      ) > 0:
          lowerCAmelCase__          =			"\n".join(lowerCAmelCase_      )
          raise Exception(
              "Found the following copy inconsistencies:\n"
              + diff
              + "\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them."      )


if __name__ == "__main__":
     UpperCamelCase								=					argparse.ArgumentParser()
     parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
     UpperCamelCase								=					parser.parse_args()

     check_copies(args.fix_and_overwrite)
