


"""simple docstring"""







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


__a			 =   -1 if torch_device == "cpu" else 0
@is_pipeline_test
class 							lowerCamelCase       (			unittest.TestCase ):


							"""simple docstring"""
							SCREAMING_SNAKE_CASE										=      MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
							SCREAMING_SNAKE_CASE										=      TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING






							def 					lowerCAmelCase     (						self :     Tuple		, lowerCamelCase_ :     Any		, lowerCamelCase_ :     str		, lowerCamelCase_ :     int				):
													UpperCAmelCase__		=    SummarizationPipeline(model=lowerCamelCase_		, tokenizer=lowerCamelCase_				)
													return summarizer, ["(CNN)The Palestinian Authority officially became", "Some other text"]






							def 					lowerCAmelCase     (						self :     Any		, lowerCamelCase_ :     str		, lowerCamelCase_ :     Dict				):
													UpperCAmelCase__		=    summarizer.model

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

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

													# Some models (Switch Transformers, LED, T5, LongT5, etc) can handle long sequences.
													UpperCAmelCase__		=    [
													    '''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(lowerCamelCase_		, lowerCamelCase_				)
																			    and get_gpu_count() > 0
																			    and len(summarizer.model.trainable_weights				) > 0
																			):
																									with self.assertRaises(lowerCamelCase_				):
																															UpperCAmelCase__		=    summarizer('''This ''' * 1000				)
													UpperCAmelCase__		=    summarizer('''This ''' * 1000		, truncation=TruncationStrategy.ONLY_FIRST				)






							@require_torch
							def 					lowerCAmelCase     (						self :     Any				):
													UpperCAmelCase__		=    pipeline(task='''summarization'''		, model='''sshleifer/tiny-mbart'''		, framework='''pt'''				)
													UpperCAmelCase__		=    summarizer('''This is a small test'''				)
													self.assertEqual(
													    lowerCamelCase_		, [
													        {
													            '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
													        }
													    ]		, )






							@require_tf
							def 					lowerCAmelCase     (						self :     str				):
													UpperCAmelCase__		=    pipeline(task='''summarization'''		, model='''sshleifer/tiny-mbart'''		, framework='''tf'''				)
													UpperCAmelCase__		=    summarizer('''This is a small test'''				)
													self.assertEqual(
													    lowerCamelCase_		, [
													        {
													            '''summary_text''': '''เข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไปเข้าไป'''
													        }
													    ]		, )






							@require_torch
							@slow
							def 					lowerCAmelCase     (						self :     Optional[Any]				):
													UpperCAmelCase__		=    pipeline(task='''summarization'''		, device=lowerCamelCase_				)
													UpperCAmelCase__		=    (
													    ''' (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.'''
													)
													UpperCAmelCase__		=    (
													    ''' 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 .'''
													)
													UpperCAmelCase__		=    summarizer(lowerCamelCase_				)
													self.assertEqual(result[0]['''summary_text''']		, lowerCamelCase_				)



"""simple docstring"""







import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

						with open(_UpperCamelCase	,			'''wb''' ) as f:
												with requests.get(_UpperCamelCase	,			stream=_UpperCamelCase ) as req:
																		for chunk in req.iter_content(chunk_size=2048 ):
																								f.write(_UpperCamelCase )






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitConfig()

						UpperCAmelCase__		=    400
						UpperCAmelCase__		=    '''huggingface/label-files'''
						UpperCAmelCase__		=    '''kinetics400-id2label.json'''

						UpperCAmelCase__		=    json.load(open(hf_hub_download(_UpperCamelCase	,			_UpperCamelCase	,			repo_type='''dataset''' )	,			'''r''' ) )
						UpperCAmelCase__		=    {int(_UpperCamelCase ): v for k, v in idalabel.items()}
						UpperCAmelCase__		=    idalabel
						UpperCAmelCase__		=    {v: k for k, v in idalabel.items()}
						return config






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    hf_hub_download(
						    repo_id='''hf-internal-testing/spaghetti-video'''	,			filename='''eating_spaghetti_32_frames.npy'''	,			repo_type='''dataset''' )
						UpperCAmelCase__		=    np.load(_UpperCamelCase )
						return list(_UpperCamelCase )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						if np.ndim(_UpperCamelCase ) == 2:
												return transform_attention_bias(_UpperCamelCase )

						elif np.ndim(_UpperCamelCase ) == 3:
												return transform_attention_kernel(_UpperCamelCase )

						else:
												raise Exception(F"""Invalid number of dimesions: {np.ndim(_UpperCamelCase )}""" )






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return current.flatten()






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0], current.shape[1] * current.shape[2]) ).T






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return np.reshape(_UpperCamelCase	,			(current.shape[0] * current.shape[1], current.shape[2]) ).T






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer'''][F"""encoderblock_{i}"""]

						UpperCAmelCase__		=    F"""encoder.layer.{i}."""
						UpperCAmelCase__		=    {
						    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
						    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel'''] ),
						    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
						    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel'''] ),
						    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
						    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
						    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
						    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
						    prefix
						    + '''attention.attention.query.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias'''] ),
						    prefix
						    + '''attention.attention.query.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias'''] ),
						    prefix
						    + '''attention.attention.key.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel'''] ),
						    prefix
						    + '''attention.attention.value.bias''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias'''] ),
						    prefix
						    + '''attention.attention.value.weight''': transform_attention(
						        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel'''] ),
						    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
						    prefix
						    + '''attention.output.dense.weight''': transform_attention_output_weight(
						        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel'''] ),
						}

						return new_state






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()] )






def 					a__ (       _UpperCamelCase	,			_UpperCamelCase=False ):





						"""simple docstring"""

						UpperCAmelCase__		=    get_n_layers(_UpperCamelCase )

						UpperCAmelCase__		=    OrderedDict()

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

						UpperCAmelCase__		=    np.transpose(
						    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,			(4, 3, 0, 1, 2) )
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''cls''']
						UpperCAmelCase__		=    state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
						    '''pos_embedding'''
						]

						for i in range(_UpperCamelCase ):
												new_state.update(transform_state_encoder_block(_UpperCamelCase	,			_UpperCamelCase ) )

						if classification_head:
												UpperCAmelCase__		=    {'''vivit.''' + k: v for k, v in new_state.items()}
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel'''] )
												UpperCAmelCase__		=    np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias'''] )

						return {k: torch.tensor(_UpperCamelCase ) for k, v in new_state.items()}






def 					a__ (       ):





						"""simple docstring"""

						UpperCAmelCase__		=    VivitImageProcessor()

						assert extractor.do_resize is True
						assert extractor.size == {"shortest_edge": 256}
						assert extractor.do_center_crop is True
						assert extractor.crop_size == {"width": 224, "height": 224}
						assert extractor.resample == PILImageResampling.BILINEAR

						# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
						# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
						# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
						assert extractor.do_normalize is False
						assert extractor.do_rescale is True
						assert extractor.rescale_factor == 1 / 255

						# zero-centering = True in original implementation
						assert extractor.do_zero_centering is True

						return extractor






def 					a__ (       _UpperCamelCase ):





						"""simple docstring"""

						UpperCAmelCase__		=    '''checkpoint'''

						if not os.path.exists(_UpperCamelCase ):
												download_checkpoint(_UpperCamelCase )

						UpperCAmelCase__		=    restore_checkpoint(_UpperCamelCase	,			_UpperCamelCase )
						UpperCAmelCase__		=    transform_state(_UpperCamelCase	,			classification_head=_UpperCamelCase )

						UpperCAmelCase__		=    get_vivit_config()

						assert config.image_size == 224
						assert config.num_frames == 32

						UpperCAmelCase__		=    VivitForVideoClassification(_UpperCamelCase )
						model.load_state_dict(_UpperCamelCase )
						model.eval()

						UpperCAmelCase__		=    get_processor()

						UpperCAmelCase__		=    prepare_video()
						UpperCAmelCase__		=    extractor(_UpperCamelCase	,			return_tensors='''pt''' )

						UpperCAmelCase__		=    model(**_UpperCamelCase )

						UpperCAmelCase__		=    torch.Size([1, 400] )
						UpperCAmelCase__		=    torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658] )

						assert outputs.logits.shape == expected_shape
						assert torch.allclose(outputs.logits[0, :5]	,			_UpperCamelCase	,			atol=1E-4 ), outputs.logits[0, :5]

						model.save_pretrained(_UpperCamelCase )
						extractor.save_pretrained(_UpperCamelCase )


if __name__ == "__main__":
						__a			 =   argparse.ArgumentParser()

						parser.add_argument("--output_model_name", "-o", type=str, help="Output path for the converted HuggingFace model")

						__a			 =   parser.parse_args()
						convert(args.output_model_name)