




import inspect
from typing import Callable, List, Optional, Set, Tuple, Union

import torch
from packaging import version
from safetensors.torch import storage_ptr, storage_size
from torch import nn

from .utils import logging


SCREAMING_SNAKE_CASE:    Any											=   [nn.LayerNorm]

SCREAMING_SNAKE_CASE:    List[str]											=   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE:    Any											=   version.parse(version.parse(torch.__version__).base_version)

SCREAMING_SNAKE_CASE:    Optional[int]											=   parsed_torch_version_base >= version.parse('''2.0''')
SCREAMING_SNAKE_CASE:    int											=   parsed_torch_version_base >= version.parse('''1.12''')
SCREAMING_SNAKE_CASE:    List[Any]											=   parsed_torch_version_base >= version.parse('''1.11''')
SCREAMING_SNAKE_CASE:    Dict											=   parsed_torch_version_base < version.parse('''1.11''')


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	self   )->  str:
		from torch import _softmax_backward_data

		if is_torch_less_than_1_11:
				return _softmax_backward_data(lowerCAmelCase						,	lowerCAmelCase						,	parent.dim						,	self   )
		else:
				return _softmax_backward_data(lowerCAmelCase						,	lowerCAmelCase						,	parent.dim						,	self.dtype   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase = 0   )->  nn.Linear:
		SCREAMING_SNAKE_CASE_							=       index.to(layer.weight.device   )
		SCREAMING_SNAKE_CASE_							=       layer.weight.index_select(lowerCAmelCase						,	lowerCAmelCase   ).clone().detach()
		if layer.bias is not None:
				if dim == 1:
						SCREAMING_SNAKE_CASE_							=       layer.bias.clone().detach()
				else:
						SCREAMING_SNAKE_CASE_							=       layer.bias[index].clone().detach()
		SCREAMING_SNAKE_CASE_							=       list(layer.weight.size()   )
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       nn.Linear(new_size[1]						,	new_size[0]						,	bias=layer.bias is not None   ).to(layer.weight.device   )
		SCREAMING_SNAKE_CASE_							=       False
		new_layer.weight.copy_(W.contiguous()   )
		SCREAMING_SNAKE_CASE_							=       True
		if layer.bias is not None:
				SCREAMING_SNAKE_CASE_							=       False
				new_layer.bias.copy_(b.contiguous()   )
				SCREAMING_SNAKE_CASE_							=       True
		return new_layer



class       lowercase_		(nn.Module   ):



						def __init__( self  :      Union[str, Any]				,       snake_case__  :      str				,       snake_case__  :      Optional[int]     ):

								"""simple docstring"""

								super().__init__()
								SCREAMING_SNAKE_CASE_							=       nf
								SCREAMING_SNAKE_CASE_							=       nn.Parameter(torch.empty(snake_case__				,       snake_case__     )     )
								SCREAMING_SNAKE_CASE_							=       nn.Parameter(torch.zeros(snake_case__     )     )
								nn.init.normal_(self.weight				,       std=0.02     )






						def   __a				( self  :      Any				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       x.size()[:-1] + (self.nf,)
								SCREAMING_SNAKE_CASE_							=       torch.addmm(self.bias				,       x.view(-1				,       x.size(-1     )     )				,       self.weight     )
								SCREAMING_SNAKE_CASE_							=       x.view(snake_case__     )
								return x


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase = 1   )->  ConvaD:
		SCREAMING_SNAKE_CASE_							=       index.to(layer.weight.device   )
		SCREAMING_SNAKE_CASE_							=       layer.weight.index_select(lowerCAmelCase						,	lowerCAmelCase   ).clone().detach()
		if dim == 0:
				SCREAMING_SNAKE_CASE_							=       layer.bias.clone().detach()
		else:
				SCREAMING_SNAKE_CASE_							=       layer.bias[index].clone().detach()
		SCREAMING_SNAKE_CASE_							=       list(layer.weight.size()   )
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       ConvaD(new_size[1]						,	new_size[0]   ).to(layer.weight.device   )
		SCREAMING_SNAKE_CASE_							=       False
		new_layer.weight.copy_(W.contiguous()   )
		SCREAMING_SNAKE_CASE_							=       True
		SCREAMING_SNAKE_CASE_							=       False
		new_layer.bias.copy_(b.contiguous()   )
		SCREAMING_SNAKE_CASE_							=       True
		return new_layer


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase = None   )->  Union[nn.Linear, ConvaD]:
		if isinstance(lowerCAmelCase						,	nn.Linear   ):
				return prune_linear_layer(lowerCAmelCase						,	lowerCAmelCase						,	dim=0 if dim is None else dim   )
		elif isinstance(lowerCAmelCase						,	lowerCAmelCase   ):
				return prune_convad_layer(lowerCAmelCase						,	lowerCAmelCase						,	dim=1 if dim is None else dim   )
		else:
				raise ValueError(F'''Can\'t prune layer of class {layer.__class__}'''   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	*lowerCAmelCase   )->  torch.Tensor:
		assert len(lowerCAmelCase   ) > 0, F'''{input_tensors} has to be a tuple/list of tensors'''

		# inspect.signature exist since python 3.5 and is a python method -> no problem with backward compatibility
		SCREAMING_SNAKE_CASE_							=       len(inspect.signature(lowerCAmelCase   ).parameters   )
		if num_args_in_forward_chunk_fn != len(lowerCAmelCase   ):
				raise ValueError(
				    F'''forward_chunk_fn expects {num_args_in_forward_chunk_fn} arguments, but only {len(lowerCAmelCase   )} input '''
				    'tensors are given'   )

		if chunk_size > 0:
				SCREAMING_SNAKE_CASE_							=       input_tensors[0].shape[chunk_dim]
				for input_tensor in input_tensors:
						if input_tensor.shape[chunk_dim] != tensor_shape:
								raise ValueError(
								    F'''All input tenors have to be of the same shape: {tensor_shape}, '''
								    F'''found shape {input_tensor.shape[chunk_dim]}'''   )

				if input_tensors[0].shape[chunk_dim] % chunk_size != 0:
						raise ValueError(
						    F'''The dimension to be chunked {input_tensors[0].shape[chunk_dim]} has to be a multiple of the chunk '''
						    F'''size {chunk_size}'''   )

				SCREAMING_SNAKE_CASE_							=       input_tensors[0].shape[chunk_dim] // chunk_size

				# chunk input tensor into tuples
				SCREAMING_SNAKE_CASE_							=       tuple(input_tensor.chunk(lowerCAmelCase						,	dim=lowerCAmelCase   ) for input_tensor in input_tensors   )
				# apply forward fn to every tuple
				SCREAMING_SNAKE_CASE_							=       tuple(forward_fn(*lowerCAmelCase   ) for input_tensors_chunk in zip(*lowerCAmelCase   )   )
				# concatenate output at same dimension
				return torch.cat(lowerCAmelCase						,	dim=lowerCAmelCase   )

		return forward_fn(*lowerCAmelCase   )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  Tuple[Set[int], torch.LongTensor]:
		SCREAMING_SNAKE_CASE_							=       torch.ones(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       set(lowerCAmelCase   ) - already_pruned_heads  # Convert to set and remove already pruned heads
		for head in heads:
				# Compute how many pruned heads are before the head and move the index accordingly
				SCREAMING_SNAKE_CASE_							=       head - sum(1 if h < head else 0 for h in already_pruned_heads   )
				SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       mask.view(-1   ).contiguous().eq(1   )
		SCREAMING_SNAKE_CASE_							=       torch.arange(len(lowerCAmelCase   )   )[mask].long()
		return heads, index


def 				_a       (				*lowerCAmelCase						,	lowerCAmelCase = None   )->  Tuple[torch.Tensor, ...]:
		return torch.meshgrid(*lowerCAmelCase						,	indexing=lowerCAmelCase   )




def 				_a       (				lowerCAmelCase   )->  Tuple[torch.device, int, int]:
		return tensor.device, storage_ptr(lowerCAmelCase   ), storage_size(lowerCAmelCase   )





import unittest

from transformers import (
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
    SummarizationPipeline,
    TFPreTrainedModel,
    pipeline,
)
from transformers.testing_utils import get_gpu_count, is_pipeline_test, require_tf, require_torch, slow, torch_device
from transformers.tokenization_utils import TruncationStrategy

from .test_pipelines_common import ANY


SCREAMING_SNAKE_CASE:    Optional[Any]											=   -1 if torch_device == '''cpu''' else 0



@is_pipeline_test
class       lowercase_		(unittest.TestCase   ):
						lowerCAmelCase__ 			=MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
						lowerCAmelCase__ 			=TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING



						def   __a				( self  :      Optional[Any]				,       snake_case__  :      Tuple				,       snake_case__  :      Tuple				,       snake_case__  :      Optional[Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       SummarizationPipeline(model=snake_case__				,       tokenizer=snake_case__     )
								return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]



						def   __a				( self  :      Tuple				,       snake_case__  :      str				,       snake_case__  :      Union[str, Any]     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       summarizer.model

								SCREAMING_SNAKE_CASE_							=       summarizer('(CNN)The Palestinian Authority officially became'     )
								self.assertEqual(snake_case__				,       [{'summary_text': ANY(snake_case__     )}]     )

								SCREAMING_SNAKE_CASE_							=       summarizer(
								    '(CNN)The Palestinian Authority officially became '				,       num_beams=2				,       min_length=2				,       max_length=5				,       )
								self.assertEqual(snake_case__				,       [{'summary_text': ANY(snake_case__     )}]     )

								# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
								SCREAMING_SNAKE_CASE_							=       [
								    'SwitchTransformersConfig',
								    'T5Config',
								    'LongT5Config',
								    'LEDConfig',
								    'PegasusXConfig',
								    'FSMTConfig',
								    'M2M100Config',
								    'ProphetNetConfig',  # positional embeddings up to a fixed maximum size (otherwise clamping the values)
								]
								if model.config.__class__.__name__ not in model_can_handle_longer_seq:
										# Too long and exception is expected.
										# For TF models, if the weights are initialized in GPU context, we won't get expected index error from
										# the embedding layer.
										if not (
										    isinstance(snake_case__				,       snake_case__     )
										    and get_gpu_count() > 0
										    and len(summarizer.model.trainable_weights     ) > 0
										):
												with self.assertRaises(snake_case__     ):
														SCREAMING_SNAKE_CASE_							=       summarizer('This ' * 10_00     )
								SCREAMING_SNAKE_CASE_							=       summarizer('This ' * 10_00				,       truncation=TruncationStrategy.ONLY_FIRST     )



						@require_torch
						def   __a				( self  :      Dict     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline(task='summarization'				,       model='sshleifer/tiny-mbart'				,       framework='pt'     )
								SCREAMING_SNAKE_CASE_							=       summarizer('This is a small test'     )
								self.assertEqual(
								    snake_case__				,       [
								        {
								            'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
								        }
								    ]				,       )



						@require_tf
						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline(task='summarization'				,       model='sshleifer/tiny-mbart'				,       framework='tf'     )
								SCREAMING_SNAKE_CASE_							=       summarizer('This is a small test'     )
								self.assertEqual(
								    snake_case__				,       [
								        {
								            'summary_text': 'เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'
								        }
								    ]				,       )






						@require_torch
						@slow
						def   __a				( self  :      Any     ):

								"""simple docstring"""

								SCREAMING_SNAKE_CASE_							=       pipeline(task='summarization'				,       device=snake_case__     )
								SCREAMING_SNAKE_CASE_							=       (
								    ' (CNN)The Palestinian Authority officially became the 123rd member of the International Criminal Court on'
								    ' Wednesday, a step that gives the court jurisdiction over alleged crimes in Palestinian territories. The'
								    ' formal accession was marked with a ceremony at The Hague, in the Netherlands, where the court is based.'
								    ' The Palestinians signed the ICC\'s founding Rome Statute in January, when they also accepted its'
								    ' jurisdiction over alleged crimes committed "in the occupied Palestinian territory, including East'
								    ' Jerusalem, since June 13, 2014." Later that month, the ICC opened a preliminary examination into the'
								    ' situation in Palestinian territories, paving the way for possible war crimes investigations against'
								    ' Israelis. As members of the court, Palestinians may be subject to counter-charges as well. Israel and'
								    ' the United States, neither of which is an ICC member, opposed the Palestinians\' efforts to join the'
								    ' body. But Palestinian Foreign Minister Riad al-Malki, speaking at Wednesday\'s ceremony, said it was a'
								    ' move toward greater justice. "As Palestine formally becomes a State Party to the Rome Statute today, the'
								    ' world is also a step closer to ending a long era of impunity and injustice," he said, according to an'
								    ' ICC news release. "Indeed, today brings us closer to our shared goals of justice and peace." Judge'
								    ' Kuniko Ozaki, a vice president of the ICC, said acceding to the treaty was just the first step for the'
								    ' Palestinians. "As the Rome Statute today enters into force for the State of Palestine, Palestine'
								    ' acquires all the rights as well as responsibilities that come with being a State Party to the Statute.'
								    ' These are substantive commitments, which cannot be taken lightly," she said. Rights group Human Rights'
								    ' Watch welcomed the development. "Governments seeking to penalize Palestine for joining the ICC should'
								    ' immediately end their pressure, and countries that support universal acceptance of the court\'s treaty'
								    ' should speak out to welcome its membership," said Balkees Jarrah, international justice counsel for the'
								    ' group. \"What\'s objectionable is the attempts to undermine international justice, not Palestine\'s'
								    ' decision to join a treaty to which over 100 countries around the world are members." In January, when'
								    ' the preliminary ICC examination was opened, Israeli Prime Minister Benjamin Netanyahu described it as an'
								    ' outrage, saying the court was overstepping its boundaries. The United States also said it "strongly"'
								    ' disagreed with the court\'s decision. \"As we have said repeatedly, we do not believe that Palestine is a'
								    ' state and therefore we do not believe that it is eligible to join the ICC," the State Department said in'
								    ' a statement. It urged the warring sides to resolve their differences through direct negotiations. "We'
								    ' will continue to oppose actions against Israel at the ICC as counterproductive to the cause of peace,"'
								    ' it said. But the ICC begs to differ with the definition of a state for its purposes and refers to the'
								    ' territories as "Palestine." While a preliminary examination is not a formal investigation, it allows the'
								    ' court to review evidence and determine whether to investigate suspects on both sides. Prosecutor Fatou'
								    ' Bensouda said her office would "conduct its analysis in full independence and impartiality." The war'
								    ' between Israel and Hamas militants in Gaza last summer left more than 2,000 people dead. The inquiry'
								    ' will include alleged war crimes committed since June. The International Criminal Court was set up in'
								    ' 2002 to prosecute genocide, crimes against humanity and war crimes. CNN\'s Vasco Cotovio, Kareem Khadder'
								    ' and Faith Karimi contributed to this report.'
								)
								SCREAMING_SNAKE_CASE_							=       (
								    ' The Palestinian Authority becomes the 123rd member of the International Criminal Court . The move gives'
								    ' the court jurisdiction over alleged crimes in Palestinian territories . Israel and the United States'
								    ' opposed the Palestinians\' efforts to join the court . Rights group Human Rights Watch welcomes the move,'
								    ' says governments seeking to penalize Palestine should end pressure .'
								)
								SCREAMING_SNAKE_CASE_							=       summarizer(snake_case__     )
								self.assertEqual(result[0]['summary_text']				,       snake_case__     )