'''simple docstring'''

from dataclasses import dataclass

A_	   = "0.1"
A_	   = "Lucia Harcekova"




@dataclass
class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			__magic_name__ :				int
			__magic_name__ :				int
			__magic_name__ :				str



			def __repr__(							self					):
									return F"""({self.offset}, {self.length}, {self.indicator})"""







class 		SCREAMING_SNAKE_CASE_					:



			"""simple docstring"""






			def __init__(							self				,			snake_case_ = 13				,			snake_case_ = 6					):
									snake_case :       List[str]				=    window_size
									snake_case :       Tuple				=    lookahead_buffer_size
									snake_case :       List[str]				=    self.window_size - self.lookahead_buffer_size



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    []
									snake_case :       int				=    ""

									# while there are still characters in text to compress
									while text:
															# find the next encoding phrase
															# - triplet with offset, length, indicator (the next encoding character)
															snake_case :       int				=    self._find_encoding_token(snake_case_				,			snake_case_					)

															# update the search buffer:
															# - add new characters from text into it
															# - check if size exceed the max search buffer size, if so, drop the
															#   oldest elements
															search_buffer += text[: token.length + 1]
															if len(snake_case_					) > self.search_buffer_size:
																					snake_case :       Tuple				=    search_buffer[-self.search_buffer_size :]

															# update the text
															snake_case :       str				=    text[token.length + 1 :]

															# append the token to output
															output.append(snake_case_					)

									return output



			def   lowerCamelCase (							self				,			snake_case_					):
									snake_case :       List[Any]				=    ""

									for token in tokens:
															for _ in range(token.length					):
																					output += output[-token.offset]
															output += token.indicator

									return output



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):

									if not text:
															raise ValueError("We need some text to work with."					)

									# Initialise result parameters to default values
									snake_case				,					snake_case :       Any				=    0, 0

									if not search_buffer:
															return Token(snake_case_				,			snake_case_				,			text[length]					)

									for i, character in enumerate(snake_case_					):
															snake_case :       Union[str, Any]				=    len(snake_case_					) - i
															if character == text[0]:
																					snake_case :       Optional[int]				=    self._match_length_from_index(snake_case_				,			snake_case_				,			0				,			snake_case_					)
																					# if the found length is bigger than the current or if it's equal,
																					# which means it's offset is smaller: update offset and length
																					if found_length >= length:
																											snake_case				,					snake_case :       Tuple				=    found_offset, found_length

									return Token(snake_case_				,			snake_case_				,			text[length]					)






			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_				,			snake_case_					):
									if not text or text[text_index] != window[window_index]:
															return 0
									return 1 + self._match_length_from_index(
									    snake_case_				,			window + text[text_index]				,			text_index + 1				,			window_index + 1					)


if __name__ == "__main__":
						from doctest import testmod

						testmod()
						# Initialize compressor class
						A_	   = LZaaCompressor(window_size=13, lookahead_buffer_size=6)

						# Example
						A_	   = "cabracadabrarrarrad"
						A_	   = lzaa_compressor.compress(TEXT)
						print(lzaa_compressor.compress("ababcbababaa"))
						A_	   = lzaa_compressor.decompress(compressed_text)
						assert decompressed_text == TEXT, "The LZ77 algorithm returned the invalid result."






'''simple docstring'''

import unittest

import requests

from transformers import MODEL_FOR_VISION_2_SEQ_MAPPING, TF_MODEL_FOR_VISION_2_SEQ_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    is_torch_available,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
						from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
						A_	   = False


if is_vision_available():
						from PIL import Image
else:




						class 		SCREAMING_SNAKE_CASE_					:



									"""simple docstring"""






									@staticmethod
									def   lowerCamelCase (							*snake_case_				,			**snake_case_					):
															pass




@is_pipeline_test
@require_vision
class 		SCREAMING_SNAKE_CASE_					(unittest.TestCase      ):



			"""simple docstring"""






			__magic_name__ :				Optional[int]   							=       MODEL_FOR_VISION_2_SEQ_MAPPING
			__magic_name__ :				Optional[int]   							=       TF_MODEL_FOR_VISION_2_SEQ_MAPPING



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_				,			snake_case_					):
									snake_case :       str				=    pipeline("image-to-text"				,			model=snake_case_				,			tokenizer=snake_case_				,			image_processor=snake_case_					)
									snake_case :       List[Any]				=    [
									    Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"					),
									    "./tests/fixtures/tests_samples/COCO/000000039769.png",
									]
									return pipe, examples



			def   lowerCamelCase (							self				,			snake_case_				,			snake_case_					):
									snake_case :       Union[str, Any]				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": ANY(snake_case_					)}],
									        [{"generated_text": ANY(snake_case_					)}],
									    ]				,			)



			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Dict				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)

									snake_case :       List[Any]				=    pipe(snake_case_				,			max_new_tokens=1					)
									self.assertEqual(
									    snake_case_				,			[{"generated_text": "growth"}]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[Any]				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-vit-gpt2"					)
									snake_case :       Tuple				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(
									    snake_case_				,			[
									        {
									            "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									        },
									    ]				,			)

									snake_case :       str				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									        [
									            {
									                "generated_text": "growthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthgrowthGOGO"
									            }
									        ],
									    ]				,			)



			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="hf-internal-testing/tiny-random-BlipForConditionalGeneration"					)
									snake_case :       Dict				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"
									snake_case :       List[Any]				=    "a photo of"

									snake_case :       List[str]				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertTrue(outputs[0]["generated_text"].startswith(snake_case_					)					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Tuple				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Tuple				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									    ]				,			)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       Any				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Union[str, Any]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Optional[Any]				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a pink pokemon pokemon with a blue shirt and a blue shirt"}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Tuple				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Tuple				=    "https://huggingface.co/datasets/sayakpaul/sample-datasets/resolve/main/pokemon.png"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cartoon of a purple character."}]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       Optional[Any]				=    pipeline("image-to-text"				,			model="Salesforce/blip-image-captioning-base"					)
									snake_case :       int				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       Tuple				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       str				=    "a photography of"

									snake_case :       int				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photography of a volcano"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[Any]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)



			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="microsoft/git-base-coco"					)
									snake_case :       Optional[Any]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       List[str]				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "a photo of a"

									snake_case :       Dict				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a photo of a tent with a tent and a tent in the background."}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)



			@unittest.skipIf(
			    not is_torch_greater_or_equal_than_1_11				,			reason="`Pix2StructImageProcessor` requires `torch>=1.11.0`."					)
			@slow
			@require_torch
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="google/pix2struct-ai2d-base"					)
									snake_case :       Optional[int]				=    "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
									snake_case :       str				=    Image.open(requests.get(snake_case_				,			stream=snake_case_					).raw					)

									snake_case :       Any				=    "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"

									snake_case :       str				=    pipe(snake_case_				,			prompt=snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "ash cloud"}]					)

									with self.assertRaises(snake_case_					):
															snake_case :       List[str]				=    pipe([image, image]				,			prompt=[prompt, prompt]					)






			@slow
			@require_tf
			def   lowerCamelCase (							self					):
									snake_case :       List[str]				=    pipeline("image-to-text"				,			model="ydshieh/vit-gpt2-coco-en"				,			framework="tf"					)
									snake_case :       Union[str, Any]				=    "./tests/fixtures/tests_samples/COCO/000000039769.png"

									snake_case :       Any				=    pipe(snake_case_					)
									self.assertEqual(snake_case_				,			[{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}]					)

									snake_case :       Optional[int]				=    pipe([image, image]					)
									self.assertEqual(
									    snake_case_				,			[
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									        [{"generated_text": "a cat laying on a blanket next to a cat laying on a bed "}],
									    ]				,			)





